Reviewerly/exHarmony huggingface dataset:
Image sets of perfectly nonlinear maps

Abstract We consider image sets of differentially d -uniform maps of finite fields. We present a lower bound on the image size of such maps and study their preimage distribution. Further, we focus on a particularly interesting case of APN maps on binary fields $$\mathbb {F}_{2^n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:msub> </mml:math> . We show that APN maps with the minimal image size are very close to being 3-to-1. We prove that for n even the image sets of several important families of APN maps are minimal, and as a consequence they have the classical Walsh spectrum. Finally, we present upper bounds on the image size of APN maps. For a non-bijective almost bent map f , these results imply $$\frac{2^n+1}{3}+1 \le |{\text {Im}}(f)| \le 2^n-2^{(n-1)/2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mfrac> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mn>3</mml:mn> </mml:mfrac> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>≤</mml:mo> <mml:mrow> <mml:mo>|</mml:mo> <mml:mtext>Im</mml:mtext> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>f</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>|</mml:mo> </mml:mrow> <mml:mo>≤</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>-</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> .
Tool-chain for supporting Privacy Risk Assessments

In a modern smart building, many aspects of the use can be monitored using sensing technologies. This enables a high number of data-driven applications used for many tasks, such as indoor comfort, energy efficiency, and space utilization. Open data sharing enables more robust data-driven applications for optimizing building operations. To enable such data sharing effort, there is a need for performing a privacy risk assessment for analyzing the inherent potential ethical and privacy risks that can be posed for occupants and the organization operating in the building. It is increasingly difficult to identify the inference capabilities of modern machine learning methods e.g. for estimating occupancy from CO2 datasets. In this paper, we design and implement an open source ontology-based tool-chain that can be used as part of the privacy assessment to identify potential privacy risks. This tool-chain takes in a model of the dataset that is being considered for sharing and creates a privacy risk report. We evaluate the tool-chain using five real-world datasets and compares the analysis with the data custodian. The results obtained show that the tool-chain can identify more risks, than a human data curator, and thus, there is a need for such a tool to support privacy risk analysis.
Spotted Hyena Optimizer: An Approach to Travelling Salesman Problems

The traveling salesman problem (TSP) is a well-known NP problem. Its main objective is to find a shortest close trip that visits all given cities, once for each. In this paper, we present an approach to Travelling Salesman Problems (TSPs) using a novel swarm-based algorithm named Spotted Hyena Optimizer (SHO). The algorithm is inspired by the social relationship between spotted hyenas and their collaborative behavior. Experimental results with four benchmark datasets in TSPLIB (Burma14, Bays29, Att48, and Berlin52) indicated that SHO algorithm could provide optimal solutions to TSPs. Even in some cases, it outperforms other metaheuristic algorithms such as GA, ACO, and PSO.
Topic attention encoder: A self-supervised approach for short text clustering

Short text clustering is a challenging and important task in many practical applications. However, many Bag-of-Word–based methods for short text clustering are often limited by the sparsity of text representation, while many sentence embedding–based methods fail to capture the document structure dependencies within a text corpus. In considerations of the shortcomings of many existing studies, a topic attention encoder (TAE) is proposed in this study. Given topics derived from corpus by the techniques of topic modelling, the cross-document information is introduced. This encoder assumes the document-topic vector to be the learning target and the concatenating vectors of the word embedding and corresponding topic-word vector to be the input. Also, a self-attention mechanism is employed in the encoder, which aims to extract weights of hidden states adaptively and encode the semantics of each short text document. With captured global dependencies and local semantics, TAE integrates the superiority of Bag-of-Word methods and sentence embedding methods. Finally, categories of benchmarking experiments were conducted by analysing three public data sets. It demonstrates that the proposed TAE outperforms many document representation benchmark methods for short text clustering.
Almost Fully Secured Lattice-Based Group Signatures with Verifier-Local Revocation

An efficient member revocation mechanism is a desirable feature when group signature schemes are applied in practical scenarios. Revocation methods, such as verifier-local revocation (VLR), provide an efficient member revocation in applications of group signatures. However, VLR-group signatures rely on a weaker security notion. On the other hand, group signature schemes for static groups gain stronger security with the full-anonymity security notion. Even though an outsider sees the secret signing keys of all group members in the full-anonymity, the signer is still anonymous. Achieving the full-anonymity for VLR group signature schemes is challenging due to the structure of secret signing keys. The secret signing keys of those schemes consist of tokens, which are used to manage revocation. The reveal of tokens may destroy the anonymity of the signers. We obtain stronger security for the lattice-based VLR group signature schemes by providing a new key generation method, which outputs revocation tokens without deriving from the members’ secret signing keys. We propose a new group signature scheme from lattices with VLR, which achieves stronger security than the previous related works. To avoid signature forgeries, we suggest a new zero-knowledge proof system that requires signers to validate themselves. Moreover, we output an efficient tracing mechanism.
Adapting Established Text Representations for Predicting Review Sentiment in Turkish

Natural Language Processing, and specifically Sentiment Analysis are still unexplored topics with respect to Turkish text. A key challenge is to extract meaningful word and paragraph representations. We provide a comprehensive overview on pre-processing and featurization methods for this problem. Our focus is on the inherent difficulties that come with analyzing Turkish real-world data from the e-commerce domain, such as inconsistent spelling or complicated morphological and grammatical structures.
Tools for developing color ramps for representing quantitative data

Color is a convenient visual attribute in data visualization that performs significant roles in data representation. However, there are several situations where colors are used inappropriately to represent quantitative data. A possible reason could be that it is not easy to develop color ramps that consider color differences. This paper describes some tools designed to support the development of color ramps to represent quantitative data. The tools help develop color ramps with uniform color differences by selecting colors from a specified color path (continuous straight lines or curved lines) in a color space at equal intervals according to some color difference formula.
Word Embedding for Emotional Analysis: An Overview

Word embedding is known as one of the fundamental tools in natural language processing. Extensive studies have been done to analyze the performance of word embedding models in different sentiment analysis tasks. However, very few works investigated the affective aspects of these models in emotion analysis area which has been growing fast recently. This article overviews and analyzes word embedding specifically for emotion analysis and presents new research directions accordingly. More particularly, after investigating the currently affective and non-affective works on word vectors, a framework for building emotional-aware word vectors is proposed.
Defining Trust in Computation

The concept of trust has become a prevalent concept in modern IT. Due to the rising of cloud computing, IoT, and social network, computer interaction has become more common. However, trust is still defined differently across those domains. It has various definition and computation method that can make understanding concept of trust difficult. This paper provides a formal description of trust that can be used to describe trust in more unified way. The formalization is constructed by using systematic literature review. Trust is defined as a Trustor's level of confidence in regard to the ability of a Trustee to provide expected result in an interaction between Trustor and Trustee. This definition provides a base to a more detailed formal description about trust form and computation. The formal definition is shown to be able to explain implementation of two trust model in cloud computing and social network.
Safe Evolution of Product Lines Using Configuration Knowledge Laws

When evolving a software product line, it is often important to ensure that we do it in a safe way, ensuring that the resulting product line remains well-formed and that the behavior of existing products is not affected. To ensure this, one usually has to analyze the different artifacts that constitute a product line, like feature models, configuration knowledge and assets. Manually analyzing these artifacts can be time-consuming and error prone, since a product line might consist of thousands of products. Existing works show that a non-negligible number of changes performed in commits deal only with the configuration knowledge, that is, the mapping between features and assets. This way, in this paper, we propose a set of algebraic laws, which correspond to bi-directional transformations for configuration knowledge models, that we can use to justify safe evolution of product lines, when only the configuration knowledge model changes. Using a theorem prover, we proved all laws sound with respect to a formal semantics. We also present a case study, where we use these laws to justify safe evolution scenarios of a non trivial industrial software product line.
Co-clustering algorithms for distributional data with automated variable weighting

This paper is concerned with the co-clustering of distribution-valued data, that is, the simultaneous partitioning of rows and columns of an input data table, the elements of which are distributions (or histograms) representing aggregate data. The first proposed method extends the double k-means algorithm to distributional data. The L2 Wasserstein distance, also known as Mallow’s distance, is used to compare distributions. To consider the different relevance of the variables characterizing the clusters, four variants of adaptive distributional double k-means are proposed. Accordingly, in the co-clustering procedure, an additional step is introduced to compute the relevance weights associated with the variables. In particular, each of the four algorithms provides i) a set of weights for the variables; ii) different sets of weights for the variables, one for each cluster (cluster-wise); iii) a double set of weights for the variables according to the decomposition of the L2 Wasserstein distance into two components; iv) different double sets of weights for the variables and distance components, one for each cluster (cluster-wise). Applications using simulated and real data demonstrate the effectiveness of the proposed algorithms and the contribution of the relevance weights to the co-clustering procedure according to the structure of the data.
Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients

Conventional image-motion based methods for structure from motion first compute optical flow, then solve for the 3D motion parameters based on the epipolar constraint, and finally recover the 3D geometry of the scene. However, errors in optical flow due to regularization can lead to large errors in 3D motion and structure. This paper investigates whether performance and consistency can be improved by avoiding optical flow estimation in the early stages of the structure-from-motion pipeline, and it proposes a new direct method based on image gradients (normal flow) only. Our main idea lies in a reformulation of the positive-depth constraint – the basis for estimating egomotion from normal flow – as a continuous piecewise differentiable function, which allows the use of well-known minimization techniques to solve for 3D motion. The 3D motion estimate is then refined and structure estimated adding a regularization based on depth. Experimental comparisons on standard synthetic datasets and the real-world driving benchmark dataset Kitti using three different optic flow algorithms show that the method achieves better accuracy in all but one case. Furthermore, it outperforms existing normal flow based 3D motion estimation techniques. Finally, the recovered 3D geometry is shown to be also very accurate.
Mix2Vec: Unsupervised Mixed Data Representation

Unsupervised representation learning on mixed data is highly challenging but rarely explored. It has to tackle significant challenges related to common issues in real-life mixed data, including sparsity, dynamics and heterogeneity of attributes and values. This work introduces an effective and efficient unsupervised deep representer called Mix2Vec to automatically learn a universal representation of dynamic mixed data with the above complex characteristics. Mix2Vec is empowered with three effective mechanisms: random shuffling prediction, prior distribution matching, and structural informativeness maximization, to tackle the aforementioned challenges. These mechanisms are implemented as an unsupervised deep neural representer Mix2Vec. Mix2Vec converts complex mixed data into vector space-based representations that are universal and comparable to all data objects and transparent and reusable for both unsupervised and supervised learning tasks. Extensive experiments on four large mixed datasets demonstrate that Mix2Vec performs significantly better than state-of-the-art deep representation methods. We also empirically verify the designed mechanisms in terms of representation quality, visualization and capability of enabling better performance of downstream tasks.
Video and Sensor-Based Rope Pulling Detection in Sport Climbing

Sport climbing is becoming an increasingly popular competitive sport as well as a recreational activity. For this reason, indoor sport climbing operators are constantly trying to improve their services and optimally use their infrastructure. One way to support such a task is to track the climbing activities performed by visitors while climbing. This paper considers a scenario in which a sensor is attached to a piece of climbing equipment that connects the climbing rope to the bolt anchors (quickdraws) and a camera is overlooking a climbing wall. Within this scenario, this paper explores two approaches to detect when a climber finishes a climb and pulls the rope from the wall: 1) a hybrid approach in which sensors and cameras are used and 2) a video-based approach where only cameras are used. The evaluation resulted in recognition precision of 91% for the hybrid and 76% for the video-based approach, respectively. This paper also discusses advantages and disadvantages of analysed approaches and points out future research directions to allow the automatic tracking of climbing activities.
Graph Embeddings in Criminal Investigation

Knowledge graphs are exploited in criminal investigation to integrate heterogeneous data sources and scale up the operational efficiency of enquiry protocols. Using a declarative perspective, protocols can be viewed as a set of data ingestion procedures and nested exact queries. This meets the probating nature of procedural justice that has to proceed from established facts. At the same time, the exact specification of queries represents a limit for enquiry protocols that can exclusively retrieve those facts in adherence to the designed queries. We then investigated the use of graph em-beddings procedures to extend the scope of a protocol by returning sub-graphs partially matching to its specification. Because exploring the entire set of sub-graphs quickly become computationally intractable, we developed an approach based on a hierarchical filtering procedure. A controlled experiment we executed has shown the feasibility of our approach.
Pick-and-Place Task using Wheeled Mobile Manipulator - A Control Design Perspective

This paper is aimed towards students and roboticists with an objective to provide the concise theoretical and applied knowledge necessary for the control design of mobile manipulator. For this purpose, topics such as kinematics, motion planning and control theory are explored. Moreover, this knowledge is integrated from the perspective of Mecanum wheeled 5-R mobile manipulator for the pick-and-place task of cube. Finally, the paper shows the successful application of the accumulated knowledge in simulated V-REP environment.
Chosen-Ciphertext Secure Multi-identity and Multi-attribute Pure FHE

A multi-identity pure fully homomorphic encryption (MIFHE) enables a server to perform arbitrary computation on the ciphertexts that are encrypted under different identities. In case of multi-attribute pure FHE (MAFHE), the ciphertexts are associated with different attributes. Clear and McGoldrick (CANS 2014) gave the first chosen-plaintext attack secure MIFHE and MAFHE based on indistinguishability obfuscation. In this study, we focus on building MIFHE and MAFHE which are secure under type 1 of chosen-ciphertext attack (CCA1) security model. In particular, using witness pseudorandom functions (Zhandry, TCC 2016) and multi-key pure FHE or MFHE (Mukherjee and Wichs, EUROCRYPT 2016) we propose the following constructions: By optimal size, we mean that the bit-length of a ciphertext is the bit-length of the message plus a security parameter multiplied with a constant. Known constructions of multi-identity(attribute) FHEs are either leveled, that is, support only bounded depth circuit evaluations or secure in a weaker CPA security model. With our new approach, we achieve both CCA1 security and evaluation on arbitrary depth circuits for multi-identity(attribute) FHE schemes.
Artificial Intelligence Applications for Event Management and Marketing

The current use of artificial intelligence technology in the event industry, its effects on the industry, and future trends are discussed within the scope of this section. The use of artificial intelligence technology that provided by big data draws attention. In the event industry, it is known that robotic applications (telepresence robots, robotic concierge, robot bartenders, robot peacekeepers, robot servers, robot deliveries, and entertainment robots), digital assistants, and chatbots are used within the scope of artificial intelligence technology. It has been determined that artificial intelligence technology offers the stakeholders opportunities to gain competitive advantage, to obtain information that can be used in marketing efforts, to enable digitalization in manual processes, to improve customer interactions, to increase event participation with lower costs, and to create added value with new products and services. It is predicted that this progress will continue in the future and the use of artificial intelligence technology in the event industry will expand.
Deep Hierarchical Non-negative Matrix Factorization for Clustering Short Text

This paper proposes a deep hierarchical Non-negative Matrix Factorization (NMF) method with Skip-Gram with Negative sampling (SGNS) to learn semantic relationships in short text data. The proposed unsupervised method learns a dense lower-order text presentation by minimizing the encoding and decoding error of factor matrices. Semantically-enriched dense text representation is constructed using the factor matrices where clusters are identified. We empirically evaluate the effectiveness of the method against the state-of-the-art short text clustering methods and deep neural embedding based methods.
Memory-Augmented Neural Networks on FPGA for Real-Time and Energy-Efficient Question Answering

Memory-augmented neural networks (MANNs) were introduced to handle long-term dependent data efficiently. MANNs have shown promising results in question answering (QA) tasks that require holding contexts for answering a given question. As demands for QA on edge devices have increased, the utilization of MANNs in resource-constrained environments has become important. To achieve fast and energy-efficient inference of MANNs, we can exploit application-specific hardware accelerators on field-programmable gate arrays (FPGAs). Although several accelerators for conventional deep neural networks have been designed, it is difficult to efficiently utilize the accelerators with MANNs due to different requirements. In addition, characteristics of QA tasks should be considered for further improving the efficiency of inference on the accelerators. To address the aforementioned issues, we propose an inference accelerator of MANNs on FPGA. To fully utilize the proposed accelerator, we introduce fast inference methods considering the features of QA tasks. To evaluate our proposed approach, we implemented the proposed architecture on an FPGA and measured the execution time and energy consumption for the bAbI data set. According to our thorough experiments, the proposed methods improved speed and energy efficiency of the inference of MANNs up to about 25.6 and 28.4 times, respectively, compared with those of CPU.
Tweakable TWINE: Building a Tweakable Block Cipher on Generalized Feistel Structure

Tweakable block cipher (TBC) is an extension of conventional block cipher. We study how to build a TBC based on generalized Feistel structure (GFS), a classical block cipher construction. While known dedicated TBC proposals are based on substitution-permutation network (SPN), GFS has not been used for building TBC. In particular, we take 64-bit GFS block cipher TWINE and try to make it tweakable with a minimum change. To find a best one from a large number of candidates, we performed a comprehensive search with a help of mixed integer linear programming (MILP) solver. As a result, our proposal TWINE is quite efficient, has the same number of rounds as TWINE with extremely simple tweak schedule.