Reviewerly/exHarmony huggingface dataset:
Multi-Party High-Dimensional Data Publishing Under Differential Privacy

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation

Massimo Poesio, Jon Chamberlain, Silviu Paun, Juntao Yu, Alexandra Uma, Udo Kruschwitz. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Legal Aspects and Emerging Risks in the Use of Smart Contracts Based on Blockchain

Although the majority application of Blockchain Technologies (BT) are in the field of cryptocurrencies, they are gradually spreading to other services where a decentralized, reliable and immutable model makes sense. One of the fields where the use of Blockchain Technologies is spreading the most is in Smart Contracts, computer programs which are executed in the blockchains establishing a collection of clauses between the participating parties that agree to interact with each other and that are executed automatically at the moment in which these clauses are fulfilled. As it is a computer code, both the client and the service provider cannot misinterpret the agreed clauses, facilitating and verifying the agreement of the contract. This article will review existing applications and reviews the main vulnerabilities of Smart Contracts deployed within Blockchain Technologies. It also reviews the legal implications of the use of these technologies.
Hybrid-adaptive differential evolution with decay function (HyDE-DF) applied to the 100-digit challenge competition on single objective numerical optimization

In this paper, a hybrid-adaptive differential evolution with a decay function (HyDE-DF)1 is proposed for numerical function optimization. The proposed HyDE-DF is applied to the 100-Digit Challenge in a set of 10 benchmark functions. Results show that HyDE-DF can achieve a 93/100 score, proving its effectiveness for numerical optimization.
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

Humans are able to perform a myriad of sophisticated tasks by drawing upon skills acquired through prior experience. For autonomous agents to have this capability, they must be able to extract reusable skills from past experience that can be recombined in new ways for subsequent tasks. Furthermore, when controlling complex high-dimensional morphologies, such as humanoid bodies, tasks often require coordination of multiple skills simultaneously. Learning discrete primitives for every combination of skills quickly becomes prohibitive. Composable primitives that can be recombined to create a large variety of behaviors can be more suitable for modeling this combinatorial explosion. In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors. Our method factorizes an agent's skills into a collection of primitives, where multiple primitives can be activated simultaneously via multiplicative composition. This flexibility allows the primitives to be transferred and recombined to elicit new behaviors as necessary for novel tasks. We demonstrate that MCP is able to extract composable skills for highly complex simulated characters from pre-training tasks, such as motion imitation, and then reuse these skills to solve challenging continuous control tasks, such as dribbling a soccer ball to a goal, and picking up an object and transporting it to a target location.
Galois hulls of linear codes over finite fields

The $$\ell $$-Galois hull $$h_{\ell }(C)$$ of an [n, k] linear code C over the finite field $${\mathbb {F}}_q$$ is the intersection of C and $$C^{{\bot }_{\ell }}$$, where $$C^{\bot _{\ell }}$$ denotes the $$\ell $$-Galois dual of C which was introduced by Fan and Zhang in 2017. The $$\ell $$-Galois LCD code is a linear code C satisfying $$h_{\ell }(C)= C\bigcap C^{\bot _{\ell }}= \{0\}$$. In this paper, we show that the dimension of the $$\ell $$-Galois hull of a linear code is invariant under permutation equivalences and we provide a method to calculate the dimension of the $$\ell $$-Galois hull through a generator matrix of the code. Moreover, we obtain that the dimension of the $$\ell $$-Galois hull of a ternary code is also invariant under monomial equivalences. We show that every [n, k] linear code over $${\mathbb {F}}_{q}$$ is monomial equivalent to an $$\ell $$-Galois LCD code for any $$q>4$$. We conclude that if there exists an [n, k] linear code over $${\mathbb {F}}_{q}$$ for any $$q>4$$, then there exists an $$\ell $$-Galois LCD code with the same parameters for any $$0\le \ell \le e-1$$, where $$q=p^e$$ for some prime number p. As an application, we characterize the $$\ell $$-Galois hulls of matrix product codes over finite fields.
Ondulé

We present Ondulé-an interactive design tool that allows novices to create parameterizable deformation behaviors in 3D-printable models using helical springs and embedded joints. Informed by spring theory and our empirical mechanical experiments, we introduce spring and joint-based design techniques that support a range of parameterizable deformation behaviors, including compress, extend, twist, bend, and various combinations. To enable users to design and add these deformations to their models, we introduce a custom design tool for Rhino. Here, users can convert selected geometries into springs, customize spring stiffness, and parameterize their design with mechanical constraints for desired behaviors. To demonstrate the feasibility of our approach and the breadth of new designs that it enables, we showcase a set of example 3D-printed applications from launching rocket toys to tangible storytelling props. We conclude with a discussion of key challenges and open research questions.
Cloud computing security taxonomy: From an atomistic to a holistic view

Countless discussions around security challenges affecting cloud computing are often large textual accounts, which can be cumbersome to read and prone to misinterpretation. The growing reliance on cloud computing means that not only should we focus on evaluating its security challenges but devote greater attention towards how challenges are viewed and communicated. With many cloud computing implementations in use and a growing evolution of the cloud paradigm (including fog, edge and cloudlets), comprehending, correlating and classifying diverse perspectives to security challenges increasingly becomes critical. Current classifications are only suited for limited use; both as effective tools for research and countermeasures design. The taxonomic approach has been used as a modeling technique towards classifying concepts across many domains. This paper surveys multiple perspectives of cloud security challenges and systematically develops corresponding graphical taxonomy based upon meta-synthesis of important cloud security concepts in literature. The contributions and significance of this work are as follows: (1) a holistic view simplifies visualization for the reader by providing illustrative graphics of existing textual perspectives, highlighting entity relationships among cloud entities/players thereby exposing security areas at every layer of the cloud. (2) a holistic taxonomy that facilitates the design of enforcement or corrective countermeasures based upon the source or origin of a security incident. (3) a holistic taxonomy highlights security boundary and identifies apt areas to implement security countermeasures.
Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection

Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high dimensional hidden dynamics and one trainable output layer. It has the nice feature of requiring limited training which is critical for certain applications where training data is extremely limited and costly to obtain. In this paper, we consider two ways to extend the shallow architecture to deep RC to improve the performance without sacrificing the underlying benefit: (1) Extend the output layer to a three layer structure which promotes a joint time-frequency processing to neuron states; (2) Sequentially stack RCs to form a deep neural network. Using the new structure of the deep RC we redesign the physical layer receiver for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) signals since MIMO-OFDM is a key enabling technology in the 5th generation (5G) cellular network. The combination of RNN dynamics and the time-frequency structure of MIMO-OFDM signals allows deep RC to handle miscellaneous interference in nonlinear MIMO-OFDM channels to achieve improved performance compared to existing techniques. Meanwhile, rather than deep feedforward neural networks which rely on a massive amount of training, our introduced deep RC framework can provide a decent generalization performance using the same amount of pilots as conventional model-based methods in 5G systems. Numerical experiments show that the deep RC based receiver can offer a faster learning convergence and effectively mitigate unknown non-linear radio frequency (RF) distortion yielding twenty percent gain in terms of bit error rate (BER) over the shallow RC structure.
HPC Container Runtimes have Minimal or No Performance Impact

HPC centers are facing increasing demand for greater software flexibility to support faster and more diverse innovation in computational scientific work. Containers, which use Linux kernel features to allow a user to substitute their own software stack for that installed on the host, are an increasingly popular method to provide this flexibility. Because standard container technologies such as Docker are unsuitable for HPC, three HPC-specific technologies have emerged: Charliecloud, Shifter, and Singularity. A common concern is that containers may introduce performance overhead. To our knowledge, no comprehensive, rigorous, HPC-focused assessment of container performance has previously been performed. Our present experiment compares the performance of all three HPC container implementations and bare metal on multiple dimensions using industry-standard benchmarks (SysBench, STREAM, and HPCG). We found no meaningful performance differences between the four environments, with the possible exception of modest variation in memory usage. These results suggest that HPC users should feel free to containerize their applications without concern about performance degradation, regardless of the container technology used. It is an encouraging development towards greater adoption of user-defined software stacks to increase the flexibility of HPC systems.
Self-Weighted Clustering With Adaptive Neighbors

Many modern clustering models can be divided into two separated steps, i.e., constructing a similarity graph (SG) upon samples and partitioning each sample into the corresponding cluster based on SG. Therefore, learning a reasonable SG has become a hot issue in the clustering field. Many previous works that focus on constructing better SG have been proposed. However, most of them follow an ideal assumption that the importance of different features is equal, which is not adapted in practical applications. To alleviate this problem, this article proposes a self-weighted clustering with adaptive neighbors (SWCAN) model that can assign weights for different features, learn an SG, and partition samples into clusters simultaneously. In experiments, we observe that the SWCAN can assign weights for different features reasonably and outperform than comparison clustering models on synthetic and practical data sets.
Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

Vehicle detection is essential for driverless systems. However, the current single sensor detection mode is no longer sufficient in complex and changing traffic environments. Therefore, this paper combines camera and light detection and ranging (LiDAR) to build a vehicle-detection framework that has the characteristics of multi adaptability, high real-time capacity, and robustness. First, a multi-adaptive high-precision depth-completion method was proposed to convert the 2D LiDAR sparse depth map into a dense depth map, so that the two sensors are aligned with each other at the data level. Then, the You Only Look Once Version 3 (YOLOv3) real-time object detection model was used to detect the color image and the dense depth map. Finally, a decision-level fusion method based on bounding box fusion and improved Dempster–Shafer (D–S) evidence theory was proposed to merge the two results of the previous step and obtain the final vehicle position and distance information, which not only improves the detection accuracy but also improves the robustness of the whole framework. We evaluated our method using the KITTI dataset and the Waymo Open Dataset, and the results show the effectiveness of the proposed depth completion method and multi-sensor fusion strategy.
Automatically Detecting Bystanders in Photos to Reduce Privacy Risks

Photographs taken in public places often contain bystanders -people who are not the main subject of a photo.These photos, when shared online, can reach a large number of viewers and potentially undermine the bystanders' privacy.Furthermore, recent developments in computer vision and machine learning can be used by online platforms to identify and track individuals.To combat this problem, researchers have proposed technical solutions that require bystanders to be proactive and use specific devices or applications to broadcast their privacy policy and identifying information to locate them in an image.We explore the prospect of a different approach -identifying bystanders solely based on the visual information present in an image.Through an online user study, we catalog the rationale humans use to classify subjects and bystanders in an image, and systematically validate a set of intuitive concepts (such as intentionally posing for a photo) that can be used to automatically identify bystanders.Using image data, we infer those concepts and then use them to train several classifier models.We extensively evaluate the models and compare them with human raters.On our initial dataset, with a 10-fold cross validation, our best model achieves a mean detection accuracy of 93% for images when human raters have 100% agreement on the class label and 80% when the agreement is only 67%.We validate this model on a completely different dataset and achieve similar results, demonstrating that our model generalizes well.
Cybersecurity and Safety Co-Engineering of Cyberphysical Systems—A Comprehensive Survey

Safeguarding both safety and cybersecurity is paramount to the smooth and trustworthy operation of contemporary cyber physical systems, many of which support critical functions and services. As safety and security have been known to be interdependent, they need to be jointly considered in such systems. As a result, various approaches have been proposed to address safety and cybersecurity co-engineering in cyber physical systems. This paper provides a comprehensive survey of safety and cybersecurity co-engineering methods, and discusses relevant open issues and research challenges. Despite the extent of the existing literature, several aspects of the subject still remain to be fully addressed.
Blind Payment Protocol for Payment Channel Networks

One of the most important problems of modern cryptocurrency networks is the problem of scaling: advanced cryptocurrencies like Bitcoin can handle around 5 transactions per second.One of the most promising solutions to this problem are second layer payment protocols: payment networks implemented on top of base cryptocurrency network layer, based on the idea of delaying publication of intermediate transactions and using base network only as a finalization layer.Such networks consist of entities that interact with the cryptocurrency system via a payment channel protocol, and can send, receive and forward payments.This paper describes a formal actor-based model of payment channel network and uses it to formulate a modified payment protocol that can be executed in the network without requiring any information about its topology and thus can hide information about financial relations between nodes.
What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Explainability is a hot topic nowadays for artificial intelligent (AI) systems. The role of machine learning (ML) models on influencing human decisions shed light on the back-box of computing systems. AI based system are more than just ML models. ML models are one element for the AI explainability’ design and needs to be combined with other elements so it can have significant meaning for people using AI systems. There are different goals and motivations for AI explainability. Regardless the goal for AI explainability, there are more to AI explanation than just ML models or algorithms. The explainability of an AI systems behavior needs to consider different dimensions: 1) who is the receiver of that explanation, 2) why that explanation is needed, and 3) in which context and other situated information the explanation is presented. Considering those three dimensions, the explanation can be effective by fitting the user needs and expectation in the right moment and format. The design of an AI explanation user experience is central for the pressing need from people and the society to understand how an AI system may impact on human decisions. In this paper, we present a literature review on AI explainability research and practices. We first looked at the computer science (CS) community research to identify the main research themes about AI explainability, or “explainable AI”. Then, we focus on Human-Computer Interaction (HCI) research trying to answer three questions about the selected publications: to whom the AI explainability is for (who), which is the purpose of the AI explanation (why), and in which context the AI explanation is presented (what + when).
A Comparative Study of Image Dehazing Algorithms

Haze poses a great challenge in modern-day applications. Removing haze is a challenging task because haze varies with the depth of the scenes in the image. Many automated systems like surveillance systems, object tracking, etc. use the dehazing methods internally to improve their overall performance in a hazy environment. Hence, recovering the original image from the hazy image is a crucial task. This paper provides a detailed survey of many algorithms that have been proposed for haze removal, which refines the image, either by using statistical observation of the scene or by network-based learning method. These algorithms have been assessed quantitatively and visually to compare based on their dehazing potential. The paper presents a comparative study of the various image dehazing methods having a different way of estimation of transmission and atmospheric light. This paper reviews different prior-based technique and learning-based technique image dehazing algorithms.
DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes. We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy. In order to evaluate the proposed framework, we conducted a comprehensive set of experiments to validate the applicability of DL-HAR. Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.