Reviewerly/exHarmony huggingface dataset:
The Never-Ending Story: On the Need for Continuous Privacy Impact Assessment

The importance of privacy by design has increased with initiatives such as the General Data Protection Regulation (GDPR). While static, design-level assessment of privacy aspects provides considerable benefits in the creation of privacy-preserving software-intensive systems, operational aspects that are difficult to predict at design-time also play a key role. This is particularly true in the instance of privacy impact or privacy risk: while existing approaches succeed fairly well in assessing the overall risk from a static design context, they are not well suited to capture risk elements that are dynamic and often impossible to foresee.In this position paper, we highlight this problem at the basis of a number of realistic motivational scenarios and outline our vision towards continuous privacy impact assessment and risk management.
Grabber: A tool to improve convergence in interactive image segmentation

Interactive image segmentation has considerably evolved from techniques that do not learn the parameters of the model to methods that pre-train a model and adapt it from user inputs during the process. However, user control over segmentation still requires significant improvements to avoid that corrections in one part of the object cause errors in other parts. We address this problem by presenting Grabber — a tool to improve convergence (user control) in interactive image segmentation. Grabber is thought to complete segmentation of some other initial method. From a given segmentation mask, Grabber quickly estimates anchor points in one orientation along the boundary of the mask and delineates an optimum contour constrained to pass through those points. The user can control the process by adding, removing, and moving anchor points. Grabber can also explore object properties from the initial coarse segmentation to improve boundary delineation. We integrate Grabber with two recent methods, a region-based approach and a pixel classification method based on deep neural networks. Extensive experiments with robot users on two datasets show in both cases that Grabber can significantly improve convergence, with faster delineation, higher effectiveness, and less user effort. The code of Grabber is available at https://github.com/LIDS-UNICAMP/grabber.
How can data from fitness trackers be obtained and analyzed with a forensic approach?

The use of Internet of Things devices is continuously increasing: People buy devices to make their lives more comfortable by using smart assistants or track sports activities and assess them. Moreover, these devices can sup-port digital investigators with valuable information when it is involved in a crime scene, since its data may provide information about the circumstances of the crime. One group of those devices are fitness trackers, which hold data such as walked steps. Accordingly, analysts can see activities, routines, and inconsistencies. We inspected three different common fitness trackers and developed a tool to analyze them in a standardized and forensically sound way. To collect data, we analyzed the Bluetooth communication, data on the phone, and internet communication. Our tool can analyze the different sources automatically and subsequently presents the results on a self-hosted web application. It is open-source and easily scalable so that developers can implement new extensions to support more than the three analyzed trackers.
Comparative Study on Abstractive Text Summarization

This paper represents a comparative study and gives an overview on some of the great research work done on abstractive text summarization. Getting a gist part from a large text document is known as text summarization. In recent years, text summarization became one of the most interesting and crucial research point in Natural Language Processing(NLP) area. Text summarization is grouped in two subparts and those are Extractive Text Summarization(ETS) and Abstractive Text Summarization(ATS). ETS is more simple than ATS. ETS is based on algorithms and it extracts the salient words or sentences from the given large text document. As opposed to, ATS generates summary by itself. It's a long time process because its includes lots of terms such as content preprocessing, word embedding, fundamental model design, discourse rules, validation of test and training data, attention mechanism, supervised, reinforcement learning and so forth. We are going to give a review on some papers about ATS and will discuss about the pros and cons of their models.
Feedback Strategies for Embodied Agents to Enhance Sign Language Vocabulary Learning

When learning sign language, feedback on accuracy is critical to vocabulary acquisition. When designing technologies to provide such visual feedback, we need to research effective ways to identify errors and present meaningful and effective feedback to learners. Motion capture technologies provide new opportunities to enhance sign language learning experiences through the presentation of visual feedback that indicates the accuracy of the signs made by learners. We designed, developed, and evaluated an embodied agent-based system for learning the location and gross motor movements of sign language vocabulary. The system presents a sign, tracks the learner's attempts at a sign, and provides visual feedback to the learner on their errors. We compared five different types of visual feedback, and in a study involving 51 participants we established that learners preferred visual feedback where their attempts at a sign were shown concurrently with the movements of the instructor with or without explicit corrections.
One-Shot Texture Retrieval Using Global Grouping Metric

Texture retrieval is widely used in the fields of fashion and e-commerce. This paper presents the problem of one-shot texture retrieval: given an example of a new reference texture, we aim to detect and segment all pixels of the same texture category within an arbitrary image. To address this problem, an OS-TR network is proposed to encode both reference and query images into a texture representation space, and a better comparison is made based on the global grouping information. Because the learned texture representation should be invariant to the spatial layout while preserving the rough semantic concepts, we introduce an adaptive directionality-aware module to finely discriminate the orderless texture details. To make full use of the global context information given only a few examples, we incorporate a grouping-attention mechanism into the relation network, resulting in the per-channel modulation of the local relation features. Extensive experiments on two benchmark datasets (i.e., the DTD and ADE20K dataset) and real scenarios demonstrate that our proposed method can achieve above-par segmentation performance and robust generalization across domains.
Smart City Design Based on an Ontological Knowledge System

Smart city has several definitions. Typically, it is an alliance of subsystems that have following objectives: improvement of quality of life of citizens, better use of limited resources and best use of existing infrastructure. Transportation as one of the most important subsystems shall be thus understood as one player working together with energy management, economy, eGovernment, and others. Synergy is the key to successful implementation. In order to be able to aim at the joint objective function and any synergy, the different subsystems must "understand" each other. Ontology has been acknowledged to be the most common tool to do that. To prepare an ontology for a domain (for example transportation) is a complicated task. In order to do that in a city, where there are several subsystems with complex behaviour is even more challenging. It is very difficult if not impossible to get experts from different fields to prepare a common ontology. In this paper we address the issue of Smart City Design and propose a pragmatic method to prepare an ontological knowledge system using the knowledge of various expert groups. A new concept, so call a knowledge matrix, is defined and used to enable cooperation of experts from different fields. We believe this can further help in implementation of any smart city projects. In order to demonstrate the approach, transportation domain is used as an example for the ontology design. The approach will be further validated within two case studies that are also introduced within this paper: Smart Evropská street in Prague and within a project Smart City – Smart Region – Smart Community, where a transport behavioristic model is being developed based on the ontology described within this paper.
A Study On Performance Improvement Of Hyperledger Fabric Through Batched Chaincode Message

This paper is to improve the performance of Hy-perledger Fabric that is one of the private blockchain open-source projects. The decentralization of data management, which has recently become important, is realized as a blockchain. Blockchain is a technology used in various fields such as data verification, storage, and banking, but performance is one of the important problems and is always mentioned. This is because a transaction must be agreed quickly and recorded in the ledger so that it can be used smoothly in systems that generate tens of thousands of transactions per second, such as banking and card use. Hyperledger fabric sends transactions generated by clients to peer nodes on the network. The transactions are hashed with the previous block in the distributed ledger. Peer nodes collect transactions received from multiple clients and execute chain code for verification, which is then sent to the orderer node and made into blocks. The block created at this time compares hash values to several peer nodes to verify the integrity of the data after verifying the block. Orderer node collects the transactions and is ordering them into the block. In this paper, we proposed to improve the performance of the hyperledger fabric by reducing network latency and the number of transfers by batch processing the process of sending and receiving chain code messages in the process of delivering transactions generated by clients for verification. The results of the performance are expressed in transactions per second(TPS), with the experiment showing that send rate(TPS) nearly doubling to 2302→4244, and throughput(TPS) increasing by about 1.5 times to 1677→2244. As a result of the experiment, send rate and throughput increased by a large margin, the batch processing of chain code messages can perform much higher performance.
Exploring Design Principles for Sharing of Personal Informatics Data on Ephemeral Social Media

People often do not receive the engagement or responses they desire when they share on broad social media platforms. Sharers are hesitant to share trivial accomplishments, and the emphasis on data often results posts that audiences find repetitive or unengaging. Ephemeral social media's focus on self-authored content and sharing trivial accomplishments has the potential to ameliorate these challenges. We explore design principles for incorporating personal informatics data like steps, heart rate, or duration in data-driven stickers as a first step towards integrating these data into ephemeral social media. We examine the effect of a sticker's presentation style, domain, domain-relevance, and background through three surveys with 506 total participants. We uncover the importance of domain-relevant backgrounds and stickers, identify the situational value of stickers styled as analogies, embellished, and badges, and demonstrate that data-driven stickers can make ephemeral content more informative and entertaining, discussing implications for platforms and tools.
Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image

Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively.
2-Phase Energy-Efficient Secure Positive Feedback Adiabatic Logic for CPA-Resistant IoT Devices

Internet of Things (IoT) devices are mostly areas constrained and operate on a limited battery supply and therefore have tight energy budgets. Lightweight cryptography (LWC) such as PRESENT-80 allows for minimal area usage and low energy for secure operations. However, CMOS implemented LWCs are vulnerable to side-channel attacks such as Correlation Power Analysis (CPA). Adiabatic Logic is an emerging circuit design technique that can reduce energy consumption and be CPA resistant. Many existing adiabatic logic families use a 4-phase clocking scheme which pays a large area penalty. Thus, in this paper, we propose 2-EE-SPFAL, a 2-phase clocking scheme implementation of an existing adiabatic family known as EE-SPFAL. We explore 2-phase sinusoidal waves in terms of energy efficiency and security. To demonstrate energy savings and security against CPA attacks we construct one round of PRESENT-80 in both CMOS and 2-EE-SPFAL. Simulations were conducted using 45nm technology in Cadence Spectre. At 12.5MHz, our results show an average energy saving of 50% between CMOS and 2-EE-SPFAL. Furthermore, we performed a CPA attack on both the CMOS and 2-EE-SPFAL implementation and determined that the CMOS key could be retrieved while the adiabatic key was kept hidden.
MMGatorAuth

The future of smart environments is likely to involve both passive and active interactions on the part of users. Depending on what sensors are available in the space, users may make use of multimodal interaction modalities such as hand gestures or voice commands. There is a shortage of robust yet controlled multimodal interaction datasets for smart environment applications. One application domain of interest based on current state-of-the-art is authentication for sensitive or private tasks, such as banking and email. We present a novel, large multimodal dataset for authentication interactions in both gesture and voice, collected from 106 volunteers who each performed 10 examples of each of a set of hand gesture and spoken voice commands chosen from prior literature (10,600 gesture samples and 13,780 voice samples). We present the data collection method, raw data and common features extracted, and a case study illustrating how this dataset could be useful to researchers. Our goal is to provide a benchmark dataset for testing future multimodal authentication solutions, enabling comparison across approaches.
Empirical Analysis of Impact of Query-Specific Customization of nDCG

In most existing works, nDCG is computed for a fixed cutoff k, i.e., [email protected] and some fixed discounting coefficient. Such a conventional query-independent way to compute nDCG does not accurately reflect the utility of search results perceived by an individual user and is thus non-optimal. In this paper, we conduct a case study of the impact of using query-specific nDCG on the choice of the optimal Learning-to-Rank (LETOR) methods, particularly to see whether using a query-specific nDCG would lead to a different conclusion about the relative performance of multiple LETOR methods than using the conventional query-independent nDCG would otherwise. Our initial results show that the relative ranking of LETOR methods using query-specific nDCG can be dramatically different from those using the query-independent nDCG at the individual query level, suggesting that query-specific nDCG may be useful in order to obtain more reliable conclusions in retrieval experiments.
Recursive Monte Carlo Search for Bridge Card Play

Computer Bridge remains a challenging obstacle for Artificial Intelligence. For the last twenty years, the state-of-the-art playing programs have been using a depth-one Monte Carlo (MC) search approach, associated with an open card solver called Double Dummy Solver (DDS). When increasing the computing resources, the MC approach reaches a plateau, and its playing level cannot be improved. In this work, we study Recursive MC (RMC) for Bridge card play. We show that, with more computing resources, this approach performs better than MC. Rather than using DDS or any domain-dependent simulator, a level N + 1 RMC consists in using a level N RMC playing program as simulator, level-zero RMC being MC. This recursion mechanism can be iterated several times at the cost of increasing the computing time, each time a recursion level is added. This work focusses on card play with no trump in duplicate with either 13 cards per player or 5 cards per player. With 13 cards per player, level-one RMC is superior to MC with a margin of 0.5 trick per card distribution on average, which is statistically significant. This is the first time RMC is applied with success to computer Bridge card play.
Structured Knowledge: Have we made progress? An extrinsic study of KB coverage over 19 years

Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.
Oversampling Techniques for Detecting Bitcoin Illegal Transactions

Bitcoin users are guaranteed to be anonymous, increasing the number of cryptocurrency trading related to crimes and fraudulent activities. While most studies about detecting illegal transactions try to distinguish trading patterns and classify them from legitimate ones, classification performance is poor since the class distributions of transaction data are highly imbalanced. In general, the Synthetic Minority Over-sampling TEchnique (SMOTE) is used to deal with class-imbalanced data, but SMOTE has a problem that it does not fully represent the diversity of the data. In this paper, we introduce another oversampling technique using Generative Adversarial Networks (GAN) to generate artificial training data for classification model. In order to verify similarity between artificial data and the actual one, oversampled dataset is evaluated with a classification model using XGBoost algorithm. We show classification performance is improved on average with synthetic data generated by both SMOTE and well-designed GAN model.
Handwritten Polynomial Equation Recognition and Simplification Using Convolutional Neural Network

Polynomials are algebraic expressions involving a sum of powers in one or more variables multiplied by its coefficients. If x is a variable, a0x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +a1x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> +a2x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-2</sup> +....+an then it's a (n) powers polynomial. Human is capable to solve this type of mathematical problems. In this work, we propose a system in which machines can achieve the cognitional skills that can understand the problem by visual context. By taking an input image of Handwritten polynomial equations and simplifies the problem by generating the answer as an output. Here machine can able to solve quadratic, cubic, quartic, quantic, sextic as well as (n) powers polynomials. This proposed work can be workable in an embedded system as well as a mobile application. In this scope for recognition purposes, we use a CNN model. This model dataset contains 21 classes, 10 numerals, and 3 mathematical operators and symbols, and 8 variables. The proposed workflow system automatically simplifies the Handwritten polynomial equation and has been done a really good performance. Developing an automatic equation recognizer and solver has been a desire of the researchers who worked in the field of NLP for many years.
Proposal of a DH optimization model

The Diffie-Hellman (DH) algorithm aims to allow two users who don't know each other before to jointly establish a shared secret key over an insecure communications channel. The DH itself is limited to the exchange of keys and since there is no mechanism which ensures the main security services such as confidentiality, integrity and authentication, the DH algorithm is therefore not immune to "man in the middle" attacks. In this article, we propose a hybrid model called S-SEJAD which combines cryptographic algorithms, DH and AES-128. The S-SEJAD model aims to improve security when exchanging keys with DH algorithm. The results show the effectiveness of strategies applied under the proposed model. Indeed, S-SEJAD outperforms the S-Wane model in time a real improvement over existing solutions such as New-two-Pass, STS and HMQV.
Quantum-inspired neuro coevolution model applied to coordination problems

In many real-world problems, some coordination between agents is necessary to enable the task to be optimally performed. However, obtaining this coordination can be challenging due to the quantity and characteristics of the agents, the dynamics of the environment and/or the complexity of the task, requiring much computation time. Furthermore, some problems require different types of agent specialization. In this case, it is very difficult for the programmers to define learning strategies and their parameters. Optimization of these parameters using standard evolutionary algorithms is also inadequate due to the high computational cost in these real multi-agent situations. The main objective of this study is therefore to propose a new neuroevolution model to be applied to agent coordination problems, termed the Quantum-Inspired Neuro Coevolution (QNCo) Model. QNCo makes use of paradigms from quantum physics and biological coevolution to evolve sub-populations of quantum individuals aiming convergence gains. The model has the capacity to autonomously obtain the best neural network topology of each agent, eliminating the need for the programmer to set this configuration. New quantum crossover and mutation operators were proposed and compared during function optimization of different dimensions. The proposed model was tested in two simulation problems, prey-predator and multi-rover tasks, and one real problem of mobile telephony coverage. The QNCo model yielded promising results compared to similar algorithms, with good solutions in terms of learning strategies and a great reduction in convergence time.
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning

Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction.