Reviewerly/exHarmony huggingface dataset:
Toward Direct Edge-to-Edge Transfer Learning for IoT-Enabled Edge Cameras

Due to advances in edge computing, image recognition via smart cameras (hereafter referred to as edge cameras) has facilitated the development of unmanned stores. However, it is very time consuming and costly to collect labeled data for training initial models for an edge camera. Although existing transfer learning can speed up model training, it must depend on a powerful centralized server with considerable human intervention, thus hindering the development of autonomous and collaborative edge learning. To address this issue, our study proposes direct edge-to-edge (e2e) collaborative transfer learning with three key technologies. The first is elite-instance-based matching to utilize and transmit only representative images for transfer learning to decrease network cost among edge cameras. The second is one-to-many e2e transfer learning, which can increase the knowledge reusability of a single source camera to build multiple target models. The last is many-to-one e2e transfer learning, which enables a target edge camera to reuse knowledge from multiple sources to further decrease the effort in labeled data collection. The experimental results show that elite-instance-based matching can effectively save up to 70% of source samples on average that need to be transmitted for initial model training, and it further improves the accuracy of existing one-to-many and many-to-one e2e transfer learning.
Dot-coded structured light for accurate and robust 3D reconstruction

Speckle dots have the advantage of easy projection, which makes them good candidate features of structured light (SL) cameras, such as Kinect v1. However, they generally yield poor accuracy due to block matching. To improve their accuracy, this paper proposes a dot-coded SL, the coding information of which is added into dot distribution. Some of the dots are arranged regularly to provide easy-to-locate corner features, while others are specially designed to form different shapes of unique identification. A Gaussian-cross module and a simplified ResNet have been proposed to conduct robust decoding. Various experiments are performed to verify the accuracy and robustness of our framework.
Heterogeneous ensemble selection for evolving data streams

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members, which helps to improve its performance. Although heterogeneous ensemble systems have achieved many successes in the batch classification setting, it is not trivial to extend them directly to the data stream setting. In this study, we propose a novel HEterogeneous Ensemble Selection (HEES) method, which dynamically selects an appropriate subset of base classifiers to predict data under the stream setting. We are inspired by the observation that a well-chosen subset of good base classifiers may outperform the whole ensemble system. Here, we define a good candidate as one that expresses not only high predictive performance but also high confidence in its prediction. Our selection process is thus divided into two sub-processes: accurate-candidate selection and confident-candidate selection. We define an accurate candidate in the stream context as a base classifier with high accuracy over the current concept, while a confident candidate as one with a confidence score higher than a certain threshold. In the first sub-process, we employ the prequential accuracy to estimate the performance of a base classifier at a specific time, while in the latter sub-process, we propose a new measure to quantify the predictive confidence and provide a method to learn the threshold incrementally. The final ensemble is formed by taking the intersection of the sets of confident classifiers and accurate classifiers. Experiments on a wide range of data streams show that the proposed method achieves competitive performance with lower running time in comparison to the state-of-the-art online ensemble methods.
Computing Compliant Anonymisations of Quantified ABoxes w.r.t. $$\mathcal {EL} $$ Policies

We adapt existing approaches for privacy-preserving publishing of linked data to a setting where the data are given as Description Logic (DL) ABoxes with possibly anonymised (formally: existentially quantified) individuals and the privacy policies are expressed using sets of concepts of the DL $$\mathcal {EL} $$ . We provide a chacterization of compliance of such ABoxes w.r.t. $$\mathcal {EL} $$ policies, and show how optimal compliant anonymisations of ABoxes that are non-compliant can be computed. This work extends previous work on privacy-preserving ontology publishing, in which a very restricted form of ABoxes, called instance stores, had been considered, but restricts the attention to compliance. The approach developed here can easily be adapted to the problem of computing optimal repairs of quantified ABoxes.
Learning Object Permanence from Video

Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanence is critical for building a model of the world, since objects in natural visual scenes dynamically occlude and contain each-other. Intensive studies in developmental psychology suggest that object permanence is a challenging task that is learned through extensive experience.
Fill in the Blanks

Providing functionality that streamlines the more tedious aspects of website interaction is of paramount importance to browsers as it can significantly improve the overall user experience. Browsers' autofill functionality exemplifies this goal, as it alleviates the burden of repetitively typing the same information across websites. At the same time, however, it also presents a significant privacy risk due to the inherent disparity between the browser's interpretation of a given web page and what users can visually perceive. In this paper we present the first, to our knowledge, comprehensive exploration of the privacy threats of autofill functionality. We first develop a series of new techniques for concealing the presence of form elements that allow us to obtain sensitive user information while bypassing existing browser defenses. Alarmingly, our large-scale study in the Alexa top 100K reveals the widespread use of such deceptive techniques for stealthily obtaining user-identifying information, as they are present in at least 5.8% of the forms that are autofilled by Chrome. Subsequently, our in-depth investigation of browsers' autofill functionality reveals a series of flaws and idiosyncrasies, which we exploit through a series of novel attack vectors that target specific aspects of browsers' behavior. By chaining these together we are able to demonstrate a novel invasive side-channel attack that exploits browser's autofill preview functionality for inferring sensitive information even when users choose to not utilize autofill. This attack affects all major Chromium-based browsers and allows attackers to probe users' autofill profiles for over a hundred thousand candidate values (e.g., credit card and phone numbers). Overall, while the preview mode is intended as a protective measure for enabling more informed decisions, ultimately it creates a new avenue of exposure that circumvents a user's choice to not divulge their information. In light of our findings, we have disclosed our techniques to the affected vendors, and have also created a Chrome extension that can prevent our attacks and mitigate this threat until our countermeasures are incorporated into browsers.
Relational graph neural network for situation recognition

Recently, situation recognition as a new challenging task for image understanding has gained great attention, which needs to simultaneously predict the main activity (verb) and its associated objects (noun entities) in a structured and detailed way. Several methods have been proposed to handle this task, but usually they cannot effectively model the relationships between the activity and the objects. In this paper, we propose a Relational Graph Neural Network (RGNN) for situation recognition, which builds a neural graph on the activity and the objects, and models the triplet relationships between the activity and pairs of objects through message passing between graph nodes. Moreover, we propose a two-stage training strategy to optimize the model. A progressive supervised learning is first adopted to obtain an initial prediction for the activity and the objects. Then, the initial predictions are refined by using a policy-gradient method to directly optimize the non-differentiable value-all metric. To verify the effectiveness of our method, we perform extensive experiments on the Imsitu dataset which is currently the only available dataset for situation recognition. Experimental results show that our approach outperforms the state-of-the-art methods on verb and value metrics, and demonstrates better relationships between the activity and the objects.
Multiple kernel clustering with pure graph learning scheme

Existing graph-based multiple kernel clustering (GMKC) methods usually adopt a hybridization scheme, termed HGMKC for short, which binds multiple kernel learning (MKL) and graph-based clustering (GBC) together, the former aims to construct a consensus kernel and the latter is used to learn an affinity graph based on the consensus kernel. Obviously, HGMKC performs graph learning and spectral clustering separately. Additionally, the performance of this hybridization is largely determined by MKL, which is empirically contrary to the fact that graph learning is the key of graph-based methods, rather than kernel learning. In this paper, we propose a pure GMKC method, dubbed PGMKC, which contains two parts, including candidate kernel graphs learning (CKGL) and kernel graph fusion (KGF). Specially, CKGL concentrates on constructing multiple candidate kernel graphs in Hilbert kernel space relying on kernel self-expressiveness; KGF leverages a flexibly auto-weighted graph fusion strategy and a connectivity regularizer to yield a consensus kernel graph directly. Compared to HGMKC, PGMKC avoids paying too much attention to consensus kernel construction, thus can focus more on affinity graph learning. Moreover, an efficient and effective optimization algorithm is developed to solve the proposed model. Experimental results on ten benchmark datasets demonstrate that the proposed PGMKC performs better than the state-of-the-art HGMKC competitors.
Unsupervised Learning of Particle Image Velocimetry

Particle Image Velocimetry (PIV) is a classical flow estimation problem which is widely considered and utilised, especially as a diagnostic tool in experimental fluid dynamics and the remote sensing of environmental flows. Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem. These supervised learning based methods are driven by large volumes of data with ground truth training information. However, it is difficult to collect reliable ground truth data in large-scale, real-world scenarios. Although synthetic datasets can be used as alternatives, the gap between the training set-ups and real-world scenarios limits applicability. We present here what we believe to be the first work which takes an unsupervised learning based approach to tackle PIV problems. The proposed approach is inspired by classic optical flow methods. Instead of using ground truth data, we make use of photometric loss between two consecutive image frames, consistency loss in bidirectional flow estimates and spatial smoothness loss to construct the total unsupervised loss function. The approach shows significant potential and advantages for fluid flow estimation. Results presented here demonstrate that our method outputs competitive results compared with classical PIV methods as well as supervised learning based methods for a broad PIV dataset, and even outperforms these existing approaches in some difficult flow cases. Codes and trained models are available at https://github.com/erizmr/UnLiteFlowNet-PIV.
Efficient MDP Analysis for Selfish-Mining in Blockchains

A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large miners can perform selfish mining -- deviate from the protocol to gain more than their fair share. Such systems are thus secure if all miners are smaller than a threshold size so their best response is following the protocol.
Overall Quality Prediction for HTTP Adaptive Streaming Using LSTM Network

HTTP Adaptive Streaming has become a popular solution for multimedia delivery nowadays. However, due to network bandwidth fluctuations, video quality strongly varies during streaming. Therefore, a key challenge in HTTP Adaptive Streaming is how to evaluate the overall quality of a streaming session. In this article, a machine learning approach is proposed for overall quality prediction, where each segment in a streaming session is represented by a set of features. Two options of the feature set are investigated. In the first option, we use four features, namely segment quality, content characteristics, stalling duration, and padding. The second option consists of three features, namely bitstream-level parameters, stalling duration, and padding. The features are fed into a Long Short Term Memory (LSTM) network that is capable of exploring temporal relations between impairment events of quality variations and stalling events. The overall quality is predicted from the outputs of the LSTM network using a linear regression module. Through experimental results, it is shown that the proposed approach achieves a high prediction performance and outperforms seven existing approaches. Especially, the second option is found to be both efficient and effective. The source code of the proposed approach has been made available to the public.
You Only Run Once: Spark Auto-Tuning From a Single Run

Tuning configurations of Spark jobs is not a trivial task. State-of-the-art auto-tuning systems are based on iteratively running workloads with different configurations. During the optimization process, the relevant features are explored to find good solutions. Many optimizers enhance the time-to-solution using black-box optimization algorithms that do not take into account any information from the Spark workloads. In this article, we present a new method for tuning configurations that uses information from one run of a Spark workload. To achieve good performance, we mine the SparkEventLog that is generated by the Spark engine. This log file contains a large amount of information from the executed application. We use this information to enhance a performance model with low-level features from the workload to be optimized. These features include Spark Actions, Transformations, and Task metrics. This process allows us to obtain application-specific workload information. With this information our system can predict sensible Spark configurations for unseen jobs, given that it has been trained with reasonable coverage of Spark applications. Experiments show that the presented system correctly produces good configurations, while achieving up to 80% speedup with respect to the default Spark configuration, and up to 12x speedup of the time-to-solution with respect to a standard Bayesian Optimization procedure.
Learning Dual Transformation Networks for Image Contrast Enhancement

In this work, we introduce a dual transformation network for single image contrast enhancement, which usually aims to improve global contrast and enrich local details. To this end, we propose two parallel branches to respectively handle the two goals by learning different kinds of transformations. Specifically, one branch aims to construct a global transformation curve to improve global contrast, while the other one directly predicts pixel offsets to enrich local details. In addition, we further design a differentiable histogram loss to provide supervised information related to the global contrast. In this way, the network training can be guided by different constraints, e.g., pixel-level mean squared error and statistics-level histogram error. Experiments demonstrate that our method can be effectively applied to various contrast conditions with favorable performance against the state-of-the-art methods.
GFI: A Formal Approach to Narrative Design and Game Research

We present the GFI framework (standing for Goals, Feedback, and Interpretation), inductively developed to address the MDA framework’s shortcomings as a lens and tool for modeling games. GFI stands parallel to MDA as a formal approach that offers bridging the gap between narrative design, game development, story analysis, and game research. Through GFI, we analyze foundational narrative design problems and systematically peek through the game design space, in order to evidence its analytical and generative traction. We believe these discussions signal GFI’s potential to elucidate the narrative design process, making it easier for researchers and practitioners to decompose, study, and design a broad class of game artifacts.
Optimizing entanglement generation and distribution using genetic algorithms

Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path towards scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers.
Closing the gap between designers and developers in a low code ecosystem

Nowadays, going digital is a must for a company to thrive and remain competitive. The digital transformation allows companies to react timely and adequately to the constantly evolving markets. This transformation is not without challenges. Among these is the growing demand for skilled software developers. Low-code platforms have risen to mitigate this pressure point by allowing people with non-programming backgrounds to craft digital systems capable of solving business relevant problems.
Lightweight Cryptography for the Internet of Things

The rate of implementation of the Internet of Things (IoT) devices is increasing drastically. With that, the security issues of these connected devices and their associated network is concerning. In some applications, a security breach in an IoT device can lead to serious ramifications. For instance, hacking into a control system of a manufacturing plant can put the entire production process to a stop; intruding on critical biomedical devices such as a pacemaker or an Implantable Cardioverter Defibrillator can potentially risk the life of the user. Therefore, the security challenges of such devices against cyber-attacks are of paramount importance and critical when it comes to determining the future success of IoT. In this paper, a systemization of knowledge regarding the lightweight cryptographic algorithms area for IoT based devices has been provided to better understand the limitation of IoT devices and their design constraints. We identified in this study not only the real-world applications of IoT devices with their constraint resources but also the security challenges and security threats related to IoT devices. Also, we provided an exhaustive survey of lightweight cryptographic algorithms proposed by various researchers. According to this survey, we recommended two lightweight algorithms to address the security needs of IoT devices.
Remote Pair Programming in Virtual Reality

There are many benefits to pair programming, including increased knowledge transfer, higher quality code, increased code comprehension, and team bonding. Unfortunately, when programmers work remotely, it becomes more challenging to collaborate. Virtual Reality (VR) technology has become increasingly popular in domains outside of software engineering. It allows humans to experience increased social presence, a key for collaboration, while still working remotely. In the last few years, and even more so recently, remote work has become very common for software engineers. In this paper, we explore remote pair programming and code comprehension in virtual reality. We conducted remote pair programming experiments with a total of 40 professional programmers. Half of the participants pair programmed using VR as a tool and the other half used a screen sharing system to collaborate. We found that programmers solved almost twice as many bugs using VR. We also found that the time taken to solve bugs was, on average, reduced when working in the VR environment than using the state of the art.
Opinion mining in higher education: a corpus-based approach

Student recruitment and retention heavily depend on student satisfaction and word of mouth voiced through surveys, social media, and review websites. The amount of online reviews makes their manual analysis intractable, revealing the need for automated approaches. The paper presents the first Serbian language corpus manually annotated for opinions in the domain of higher education. Agreement among annotators was calculated using the Fleiss' kappa and agr metrics. Statistical and linguistic analyses of the corpus revealed information useful for hand-crafted rules for sentiment analysis. Using developed corpus, dictionary- and machine learning-based approaches to sentiment analysis are benchmarked. Both exhibit high performance.