Reviewerly/exHarmony huggingface dataset:
Making Encryption Feel Secure: Investigating how Descriptions of Encryption Impact Perceived Security

When communication about security to end users is ineffective, people frequently misinterpret the protection offered by a system.The discrepancy between the security users perceive a system to have and the actual system state can lead to potentially risky behaviors.It is thus crucial to understand how security perceptions are shaped by interface elements such as text-based descriptions of encryption.This article addresses the question of how encryption should be described to non-experts in a way that enhances perceived security.We tested the following within-subject variables in an online experiment (N=309): a) how to best word encryption, b) whether encryption should be described with a focus on the process or outcome, or both c) whether the objective of encryption should be mentioned d) when mentioning the objective of encryption, how to best describe it e) whether a hash should be displayed to the user.We also investigated the role of context (between subjects).The verbs "encrypt" and "secure" performed comparatively well at enhancing perceived security.Overall, participants stated that they felt more secure not knowing about the objective of encryption.When it is necessary to state the objective, positive wording of the objective of encryption worked best.We discuss implications and why using these results to design for perceived lack of security might be of interest as well.This leads us to discuss ethical concerns, and we give guidelines for the design of user interfaces where encryption should be communicated to end users.
Diffusional Side-Channel Leakage From Unrolled Lightweight Block Ciphers: A Case Study of Power Analysis on PRINCE

This study investigates a new side-channel leakage observed in the inner rounds of an unrolled hardware implementation of block ciphers in a chosen-input attack scenario.The side-channel leakage occurs in the first round and it can be observed in the later inner rounds because it arises from path activation bias caused by the difference between two consecutive inputs.Therefore, a new attack that exploits the leakage is possible even for unrolled implementations equipped with countermeasures (masking and/or deglitchers that separate the circuit in terms of glitch propagation) in the round involving the leakage.We validate the existence of such a unique sidechannel leakage through a set of experiments with a fully unrolled PRINCE cipher hardware, implemented on a fieldprogrammable gate array (FPGA).In addition, we verify the validity and evaluate the hardware cost of a countermeasure for the unrolled implementation, namely the Threshold Implementation (TI) countermeasure.
EPYNET: Efficient Pyramidal Network for Clothing Segmentation

Soft biometrics traits extracted from a human body, including the type of clothes, hair color, and accessories, are useful information used for people tracking and identification.Semantic segmentation of these traits from images is still a challenge for researchers because of the huge variety of clothing styles, layering, shapes, and colors.To tackle these issues, we proposed EPYNET, a framework for clothing segmentation.EPYNET is based on the Single Shot MultiBox Detector (SSD) and the Feature Pyramid Network (FPN) with the EfficientNet model as the backbone.The framework also integrates data augmentation methods and noise reduction techniques to increase the accuracy of the segmentation.We also propose a new dataset named UTFPR-SBD3, consisting of 4,500 manually annotated images into 18 classes of objects, plus the background.Unlike available public datasets with imbalanced class distributions, the UTFPR-SBD3 has, at least, 100 instances per class to minimize the training difficulty of deep learning models.We introduced a new measure of dataset imbalance, motivated by the difficulty in comparing different datasets for clothing segmentation.With such a measure, it is possible to detect the influence of the background, classes with small items, or classes with a too high or too low number of instances.Experimental results on UTFPR-SBD3 show the effectiveness of EPYNET, outperforming the state-of-art methods for clothing segmentation on public datasets.Based on these results, we believe that the proposed approach can be potentially useful for many real-world applications related to soft biometrics, people surveillance, image description, clothes recommendation, and others.
Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems

Efficient optimisation algorithms have become important tools for finding high-quality solutions to hard, real-world problems such as production scheduling, timetabling, or vehicle routing. These algorithms are typically “black boxes” that work on mathematical models of the problem to solve. However, many problems are difficult to fully specify, and require a “human in the loop” who collaborates with the algorithm by refining the model and guiding the search to produce acceptable solutions. Recently, the Problem-Solving Loop was introduced as a high-level model of such interactive optimisation. Here, we present and evaluate nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop. They range from the choice of visual representation for solutions and constraints to the use of a solution gallery to support exploration of alternate solutions. We first examined the applicability of the recommendations by investigating how well they had been supported in previous interactive optimisation tools. We then evaluated the recommendations in the context of the vehicle routing problem with time windows (VRPTW). To do so we built a sophisticated interactive visual system for solving VRPTW that was informed by the recommendations. Ten participants then used this system to solve a variety of routing problems. We report on participant comments and interaction patterns with the tool. These showed the tool was regarded as highly usable and the results generally supported the usefulness of the underlying recommendations.
“Oh, that’s where you are!” – Towards a Hybrid Asymmetric Collaborative Immersive Analytics System

We present a hybrid Immersive Analytics system to support asymmetrical collaboration between a pair of users during synchronous data exploration. The system consists of an immersive Virtual Reality application, a non-immersive web application, and a real-time communication interface connecting both applications to provide features to facilitate the collaborators’ mutual understanding and their ability to make (spatial) references. We conducted a real world case study with pairs of language students, encouraging them to use the developed system to investigate a large multivariate Twitter dataset from a sociolinguistic perspective within an explorative analysis scenario. Based on the results of usability scores, log file analyses, observations, and interviews, we were able to validate the approach in general, and gain insights into the users’ collaboration with respect to awareness, deixis, and group dynamics.
Distributed Graph Analytics with Datalog Queries in Flink

Large-scale, parallel graph processing has been in demand over the past decade. Succinct program structure and efficient execution are among the essential requirements of graph processing frameworks. In this paper, we present Cog, which executes Datalog programs on the Apache Flink distributed dataflow system. We chose Datalog for its compact program structure and Flink for its efficiency. We implemented a parallel semi-naive evaluation algorithm exploiting Flink's delta iteration to propagate only the tuples that need to be further processed to the subsequent iterations. Flink's delta iteration feature reduces the overhead present in acyclic dataflow systems, such as Spark, when evaluating recursive queries, hence making it more efficient. We demonstrated in our experiments that Cog outperformed BigDatalog, the state-of-the-art distributed Datalog evaluation system, in most of the tests.
The Digital Docent: XR storytelling for a Living History Museum

In this work, we describe the use of a digital docent: a 3D avatar, presented using virtual and augmented reality, as a means for providing interactive storytelling experiences at a living history museum. To allow flexibility depending on the user’s location and access to technology, the app is designed to provide a common experience supporting a variety of different delivery modalities including AR devices, mobile AR, and VR on the Web.
Robustness Verification for Classifier Ensembles

We give a formal verification procedure that decides whether a classifier ensemble is robust against arbitrary randomized attacks. Such attacks consist of a set of deterministic attacks and a distribution over this set. The robustness-checking problem consists of assessing, given a set of classifiers and a labelled data set, whether there exists a randomized attack that induces a certain expected loss against all classifiers. We show the NP-hardness of the problem and provide an upper bound on the number of attacks that is sufficient to form an optimal randomized attack. These results provide an effective way to reason about the robustness of a classifier ensemble. We provide SMT and MILP encodings to compute optimal randomized attacks or prove that there is no attack inducing a certain expected loss. In the latter case, the classifier ensemble is provably robust. Our prototype implementation verifies multiple neural-network ensembles trained for image-classification tasks. The experimental results using the MILP encoding are promising both in terms of scalability and the general applicability of our verification procedure.
A Portable Hong Kong Sign Language Translation Platform with Deep Learning and Jetson Nano

As hearing loss is arousing more and more public concern, different researches have been conducted on translating the sign language into spoken language. However, most of these researches remain in a theoretical level and few of them investigate how to realize a real system. In this paper, we introduce an effective and portable Hong Kong sign language recognition platform which can translate the Hong Kong sign language within a few seconds. In this platform, there are mainly two parts: a mobile application and a Jetson Nano. The mobile application accounts for preprocessing the sign video and transferring the videos to Jetson Nano. Then, Jetson Nano will translate sign videos into spoken language with the pretrained deep learning model and return the results to the mobile application. With this platform, non-disabled people can easily translate and understand the sign performed by deaf people through mobile phones quickly. We believe that this platform can significantly facilitate the daily communication between deaf people and the others in Hong Kong.
Fully-automated functional region annotation of liver via a 2.5D class-aware deep neural network with spatial adaptation

Automatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landmarks or the vascular tree often fail to extract accurate liver segments. Furthermore, these methods are difficult to fully automate and thus remain time-consuming. To address these issues, in this study we aim to develop a fully-automated approach for functional region annotation of liver using deep learning based on 2.5D class-aware deep neural networks with spatial adaptation. 112 CT scans were fed into our 2.5D class-aware deep neural network with spatial adaptation for automatic functional region annotation of liver. The proposed model was built upon the ResU-net architecture, which adaptively selected a stack of adjacent CT slices as input and, generating masks corresponding to the center slice, automatically annotated the liver functional region from abdominal CT images. Furthermore, to minimize the problem of class-level ambiguity between different slices, the anatomy class-specific information was used. The final algorithm performance for automatic functional region annotation of liver showed large overlap with that of manual reference segmentation. The dice similarity coefficient of hepatic segments achieved high scores and an average dice score of 0.882. The entire calculation time was quite fast (~5 s) compared to manual annotation (~2.5 hours). The proposed models described in this paper offer a feasible solution for fully-automated functional region annotation of liver from CT images. The experimental results demonstrated that the proposed method can attain a high average dice score and low computational time. Therefore, this work should allow for improved liver surgical resection planning by our precise segmentation and simple fully-automated method.
Controlled Query Evaluation in Ontology-Based Data Access

In this paper we study the problem of information disclosure in ontology-based data access (OBDA). Following previous work on Controlled Query Evaluation, we introduce the framework of Policy-Protected OBDA (PPOBDA), which extends OBDA with data protection policies specified over the ontology and enforced through a censor, i.e., a function that alters answers to users' queries to avoid the disclosure of protected data. We consider PPOBDA systems in which the ontology is expressed in $$\textsc {owl\,2\,ql} $$ and the policies are denial constraints, and show that query answering under censors in such a setting can be reduced to standard query answering in OBDA (without data protection policies). The basic idea of our approach is to compile the policies of a PPOBDA system into the mapping of a standard OBDA system. To this aim, we analyze some notions of censor proposed in the literature, show that they are not suited for the above-mentioned compilation, and provide a new definition of censor that enables the effective realization of our idea. We have implemented our technique and evaluated it over the NPD benchmark for OBDA. Our results are very promising and show that controlled query evaluation in OBDA can be realized in the practice by using off-the-shelf OBDA engines.
WISE: Workload-Aware Partitioning for RDF Systems

Masses of large-scale knowledge graphs on various domains have sprung up in recent years. They are no longer able to be managed on a single machine. The distributed RDF systems intervene in the scalability issue using partitioning techniques. However, most of these systems are unaware of query workload and employ static partitioning. As diverse and dynamic workloads keep emerging in the knowledge graph applications, they cannot consistently provide good performance. To address the problem, we propose a workload-aware partitioning framework WISE, which could be deployed on any initial partitioning. It encodes the incoming SPARQL queries in a novel structure called query span and periodically examines the query span to identify the frequent query patterns. The triples of a frequent query pattern are moved to the same partition, aiming at improving the response time in the future. Our experiments on various RDF datasets and workloads indicate that WISE achieves dramatic communication reduction and considerable performance improvement over the baseline method. The migration overhead only accounts for a small portion of the total runtime.
LM4KG: Improving Common Sense Knowledge Graphs with Language Models

Language Models (LMs) and Knowledge Graphs (KGs) are both active research areas in Machine Learning and Semantic Web. While LMs have brought great improvements for many downstream tasks on their own, they are often combined with KGs providing additionally aggregated, well structured knowledge. Usually, this is done by leveraging KGs to improve LMs. But what happens if we turn this around and use LMs to improve KGs? In this paper, we propose a method enabling the use of the knowledge inherently encoded in LMs to automatically improve explicit knowledge represented in common sense KGs. Edges in these KGs represent relations between concepts, but the strength of the relations is often not clear. We propose to transform KG relations to natural language sentences, allowing us to utilize the information contained in large LMs to rate these sentences through a new perplexity-based measure, Refined Edge WEIGHTing (REWEIGHT). We test our scoring scheme REWEIGHT on the popular LM BERT to produce new weights for the edges in the well-known ConceptNet KG. By retrofitting existing word embeddings to our modified ConceptNet, we create ConceptNet NumBERTbatch embeddings and show that these outperform the original ConceptNet Numberbatch on multiple established semantic similarity datasets.
Adaptive Wasserstein Hourglass for Weakly Supervised RGB 3D Hand Pose Estimation

The deficiency of labeled training data is one of the bottlenecks in 3D hand pose estimation from monocular RGB images. Synthetic datasets have a large number of images with precise annotations, but their obvious difference with real-world datasets limits the generalization ability. Few efforts have been made to bridge the gap between the two domains in terms of their large differences. In this paper, we propose a domain adaptation method called Adaptive Wasserstein Hourglass for weakly-supervised 3D hand pose estimation to close the large gap between synthetic and real-world datasets flexibly. Adaptive Wasserstein Hourglass utilizes a feature similarity metric to identify the differences and explore the common features (e.g., hand structure) of the two datasets. Common features are drawn close adaptively during the training, whereas domain-specific features retain the differences. Learning common features helps the network in focusing on pose-related information, whereas maintaining domain-specific features reduces the optimization difficulty when closing the big gap between two domains. Extensive evaluations on two benchmark datasets demonstrate that our method succeeds in distinguishing different features and achieves optimal results when compared with state-of-the-art 3D pose estimation approaches and domain adaptation methods.
An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

JPEG-based compression is the most widely used image compression algorithm. Previously, JPEG-XT-based work focused on 16-bit depth high-dynamic satellite infrared images, but not on medical images. In this study, we represent an optimized JPEG-XT method (OPT_JPEG-XT) that better compresses 16-bit depth medical images by amplifying (N times) discrete cosine transform (DCT) coefficients. The results show that the small integers and the first two decimal portions of DCT coefficients play important roles in the compression of medical images. By using the appropriate N and number of decimal portions (NDP), OPT_JPEG-XT could realize lossless compression of medical images. Regarding upper and lower 8-bit subimages, the upper subimages have more important roles in the improvement of OPT_JPEG-XT performance than the lower subimages; lower subimages could occupy over 90% sizes of the entire encoding files. Thus, OPT_JPEG-XT could save about 60% of storage space with high PSNR (peak signal-noise-ratio, over 100) and low MSE (mean-square-error, less than 0.08) by decreasing the compression efficiency of lower subimages. Compared to the conventional JPEG-XT and JPEG 2000, OPT_JPEG-XT can acquire a similar compression performance (PSNR > 100, MSE < 0.9, SSR (saving space rate, >60%) to JPEG 2000 when using N = 20 for lower subimages and lossless compression of upper subimages. OPT_JPEG-XT could obtain high SSR (about 90%, similar to traditional JPEG-XT) with with much smaller MSE (25 times lower than conventional JPEG-XT). Therefore, OPT_JPEG-XT could be developed a novel compression method that could realize the lossless and lossy compression of medical images.
A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems

In this paper, we study 5G Channel State Information feedback reporting. We show that a Deep Learning approach based on Convolutional Neural Networks can be used to learn efficient encoding and decoding algorithms. We set up a fully compliant link level 5G-New Radio simulator with clustered delay line channel model and we consider a realistic scenario with multiple transmitting/receiving antenna schemes and noisy downlink channel estimation. Results show that our Deep Learning approach achieves results comparable with traditional methods and can also outperform them in some conditions.
Social mining for terroristic behavior detection through Arabic tweets characterization

In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.
Getting into someone else's <i>soul</i>: communicating embodied experience

The idea of connecting with the sensory realities of others can help us to build empathic ties and to think outside the boundaries of our preconceived ideas. Envisioning these opportunities, this paper introduces a framework with a set of embodied modalities derived from communicating embodied experiences through a prototype for storytelling called SOUL. This prototype was built to transmit some experiential qualities based on a first-person narrative truthfully. Three ways of bodily connecting with the experience of others are here identified as (1) assertion, (2) negotiation or (3) dissension, and described as different ways of generating embodied connections with the prototype. This approach is intended to enrich the value of alterity in HCI, including disagreement and tension in times where the design of one size fits all artefacts appear as the general rule. We conclude that this sensory way of transmitting meaning can be useful for communicating assertive messages.