Reviewerly/exHarmony huggingface dataset:
The Exchange Attack: How to Distinguish Six Rounds of AES with $$2^{88.2}$$ Chosen Plaintexts

In this paper we present exchange-equivalence attacks which is a new cryptanalytic attack technique suitable for SPN-like block cipher designs. Our new technique results in the first secret-key chosen plaintext distinguisher for 6-round AES. The complexity of the distinguisher is about $$2^{88.2}$$ in terms of data, memory and computational complexity. The distinguishing attack for AES reduced to six rounds is a straight-forward extension of an exchange attack for 5-round AES that requires $$2^{30}$$ in terms of chosen plaintexts and computation. This is also a new record for AES reduced to five rounds. The main result of this paper is that AES up to at least six rounds is biased when restricted to exchange-invariant sets of plaintexts.
Data Disclosure Under Perfect Sample Privacy

Perfect data privacy seems to be in fundamental opposition to the economical and scientific opportunities related to extensive data exchange. This paper defies this intuition by developing the principle of synergistic disclosure, in which collective properties of datasets are revealed without compromising the privacy of individual data samples. We study the properties of optimal strategies/mappings on finite as well as asymptotically large datasets, and discuss its fundamental limits defined as the synergistic disclosure capacity. Furthermore, we present explicit analytical expressions for the synergistic disclosure capacity of large datasets in various scenarios, and present cases in which our approach can disclose most of the information of interest. We finally discuss suboptimal schemes to provide sample privacy guarantees to large datasets at a reduced computational cost.
Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors

Quantum computers threaten to break public-key cryptography schemes such as DSA and ECDSA in polynomial time, which poses an imminent threat to secure signal processing. Ring learning with error (RLWE) lattice-based cryptography (LBC) is one of the most promising families of post-quantum cryptography (PQC) schemes in terms of efficiency and versatility. Two conventional methods to compute polynomial multiplication, the most compute-intensive routine in the RLWE schemes, are convolutions and Number Theoretic Transform (NTT).In this work, we explore the energy efficiency of polynomial multiplier using systolic architecture for the first time. As an early exploration, we design two high-throughput systolic array polynomial multipliers, including NTT-based and convolution-based, and compare them to our low-cost sequential (non-systolic) NTT-based multiplier. Our sequential NTT-based multiplier achieves 3x speedup over the state-of-the-art FGPA implementation of the polynomial multiplier in the NewHope-Simple key exchange mechanism on a low-cost Artix7 FPGA. When synthesized on a Zynq UltraScale+ FPGA, the NTT-based systolic and convolution-based systolic designs achieve on average 1.7x and 7.5x speedup over our sequential NTT-based multiplier respectively, which can lead to generating over 2x more signatures per second by CRYSTALS-Dilithium, a PQC digital signature scheme. These explorations help designers select the right PQC implementations for making future signal processing applications quantum-resistant.
Facing the Unplanned Migration of Serverless Applications

Serverless computing focuses on developing cloud applications that comprise components fully managed by providers. Function-as-a-Service (FaaS) service model is often associated with the term serverless as it allows developing entire applications by composing provider-managed, event-driven code snippets. However, such reduced control over the infrastructure and tight-coupling with provider's services amplify the various lock-in problems. In this work, we explore the challenges of migrating serverless, FaaS-based applications across cloud providers. To achieve this, we conduct an experiment in which we implement four prevalent yet intentionally simple serverless use cases and manually migrate them across three popular commercial cloud providers. The results show that even when migrating simple use cases, developers encounter multiple aspects of a lock-in problem. Moreover, we present a categorization of the problems and discuss the feasibility of possible solutions.
Appearance-Based Loop Closure Detection with Scale-Restrictive Visual Features

In this paper, an appearance-based loop closure detection pipeline for autonomous robots is presented. Our method uses scale-restrictive visual features for image representation with a view to reduce the computational cost. In order to achieve this, a training process is performed, where a feature matching technique indicates the features’ repeatability with respect to scale. Votes are distributed into the database through a nearest neighbor method, while a binomial probability function is responsible for the selection of the most suitable loop closing pair. Subsequently, a geometrical consistency check on the chosen pair follows. The method is subjected into an extensive evaluation via a variety of outdoor, publicly-available datasets revealing high recall rates for 100$$\%$$ precision, as compared against its baseline version, as well as, other state-of-the-art approaches.
EEG &amp; Eye Tracking User Experiments for Spatial Memory Task on Maps

The aim of this research is to evaluate the use of ET and EEG for studying the cognitive processes of expert and novice map users and to explore these processes by comparing two types of spatial memory experiments through cognitive load measurements. The first experiment consisted of single trials and participants were instructed to study a map stimulus without any time constraints in order to draw a sketch map afterwards. According to the ET metrics (i.e., average fixation duration and the number of fixations per second), no statistically significant differences emerged between experts and novices. A similar result was also obtained with EEG Frontal Alpha Asymmetry calculations. On the contrary, in terms of alpha power across all electrodes, novices exhibited significantly lower alpha power, indicating a higher cognitive load. In the second experiment, a larger number of stimuli were used to study the effect of task difficulty. The same ET metrics used in the first experiment indicated that the difference between these user groups was not statistically significant. The cognitive load was also extracted using EEG event-related spectral power changes at alpha and theta frequency bands. Preliminary data exploration mostly suggested an increase in theta power and a decrease in alpha power.
Sponges Resist Leakage: The Case of Authenticated Encryption

In this work we advance the study of leakage-resilient Authenticated Encryption with Associated Data (AEAD) and lay the theoretical groundwork for building such schemes from sponges. Building on the work of Barwell et al. (ASIACRYPT 2017), we reduce the problem of constructing leakage-resilient AEAD schemes to that of building fixed-input-length function families that retain pseudorandomness and unpredictability in the presence of leakage. Notably, neither property is implied by the other in the leakage-resilient setting. We then show that such a function family can be combined with standard primitives, namely a pseudorandom generator and a collision-resistant hash, to yield a nonce-based AEAD scheme. In addition, our construction is quite efficient in that it requires only two calls to this leakage-resilient function per encryption or decryption call. This construction can be instantiated entirely from the T-sponge to yield a concrete AEAD scheme which we call $${ \textsc {Slae}}$$. We prove this sponge-based instantiation secure in the non-adaptive leakage setting. $${ \textsc {Slae}}$$ bears many similarities and is indeed inspired by $${ \textsc {Isap}}$$, which was proposed by Dobraunig et al. at FSE 2017. However, while retaining most of the practical advantages of $${ \textsc {Isap}}$$, $${ \textsc {Slae}}$$ additionally benefits from a formal security treatment.
Cooperation Learning From Multiple Social Networks: Consistent and Complementary Perspectives

GWI survey1 has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively. Indeed, users tend to express their views in more than one social media site. Hence, merging social signals of the same user across different social networks together, if available, can facilitate the downstream analyses. Previous work has paid little attention on modeling the cooperation among the following factors when fusing data from multiple social networks: 1) as data from different sources characterizes the characteristics of the same social user, the source consistency merits our attention; 2) due to their different functional emphases, some aspects of the same user captured by different social networks can be just complementary and results in the source complementarity; and 3) different sources can contribute differently to the user characterization and hence lead to the different source confidence. Toward this end, we propose a novel unified model, which co-regularizes source consistency, complementarity, and confidence to boost the learning performance with multiple social networks. In addition, we derived its theoretical solution and verified the model with the real-world application of user interest inference. Extensive experiments over several state-of-the-art competitors have justified the superiority of our model.1http://tinyurl.com/zk6kgc9.
Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition

In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors. The existing zero-shot methods use mainly 2 kinds of representation as semantic vectors, attribute vector and embedding word vector. However, few zero-shot activity recognition methods based on embedding vector have been studied; especially for sensor-based activity recognition, no such studies exist, to the best of our knowledge. In this paper, we compare and thoroughly evaluate the Zero-shot method with different semantic vectors: (1) attribute vector, (2) embedding vector, and (3) expanded embedding vector and analyze their correlation to performance. Our results indicate that the performance of the three spaces is similar but the use of word embedding leads to a more efficient method, since this type of semantic vector can be generated automatically. Moreover, our suggested method achieved higher accuracy than attribute-vector methods, in cases when there exist similar information in both the given sensor data and in the semantic vector; the results of this study help select suitable classes and sensor data to build a training dataset.
Stochastic Gradient Descent–Whale Optimization Algorithm-Based Deep Convolutional Neural Network To Crowd Emotion Understanding

Abstract Crowd emotion understanding is an interesting research area that assists the security personnel to read the emotion/activity of the crowd in the locality. Most of the traditional methods utilize the low-level visual features to understand the crowd emotions that extend the gap between the low- and the high-level features. With the aim to develop an automatic method for emotion recognition, this paper utilizes the deep convolutional neural network (deep CNN). For the effective emotion recognition, it is essential to select the key frames of the video using the wavelet-based Bhattacharya distance. The key frames are fed to the space-time interest points descriptor that extracts the features and forms the input vector to the classifier. Deep CNN is trained using the proposed Stochastic Gradient Descent–Whale Optimization Algorithm, which is the unification of the standard stochastic gradient descent algorithm with whale optimization algorithm. The proposed classifier recognizes the emotions of the crowd, such as angry, escape, fight, happy, normal, running/walking and violence. The analysis of the method proved that the proposed approaches acquired a maximal accuracy, specificity and sensitivity of 0.9693, 0.9936 and 0.9675, respectively.
Next POI Recommendation via Graph Embedding Representation From H-Deepwalk on Hybrid Network

With the rapid development of location-based social networks (LBSNs), point of interest (POI) recommendation has become more and more popular personalized service. Cold start problem and poor interpretability are two main challenges of the traditional recommendation system. In this paper, we propose a novel social and sequence-aware next POI recommendation model. Our model utilizes an improved DeepWalk on heterogeneous network to learn better POI representation which contains social relationship and geographical influence. In this way, the cold start problem and interpretability problem can be solved to some extend. As for the user preference learning, we use LSTM (Long Short-Term Memory) mechanism to infer user interest, taking both long term and short term intentions into consideration. The model is trained in a metric learning framework. The experimental results on two real LBSN datasets show our model has outstanding performance in terms of AUC, Recall, and ACC. Furthermore, we can obtain 20% performance improvement on spare dataset which indicates that our model can well solve the cold start problem.
Iterative Reconstruction of Signals on Graph

We propose an iterative algorithm to interpolate graph signals from only a partial set of samples. Our method is derived from the well known Papoulis-Gerchberg algorithm by considering the optimal value of a constant involved in the iteration step. Compared with existing graph signal reconstruction algorithms, the proposed method achieves similar or better performance both in terms of convergence rate and computational efficiency.
Making interaction with virtual reality accessible: rendering and guiding methods for subtitles

Abstract Accessibility in immersive media is a relevant research topic, still in its infancy. This article explores the appropriateness of two rendering modes (fixed-positioned and always-visible) and two guiding methods (arrows and auto-positioning) for subtitles in 360° video. All considered conditions have been implemented and integrated in an end-to-end platform (from production to consumption) for their validation and evaluation. A pilot study with end users has been conducted with the goals of determining the preferred options by users, the options that result in a higher presence, and of gathering extra valuable feedback from the end users. The obtained results reflect that, for the considered 360° content types, always-visible subtitles were more preferred by end users and received better results in the presence questionnaire than the fixed-positioned subtitles. Regarding guiding methods, participants preferred arrows over auto-positioning because arrows were considered more intuitive and easier to follow and reported better results in the presence questionnaire.
Digital sextortion: Internet predators and pediatric interventions

Purpose of review Sextortion is defined as the act of coercing people into sending explicit images of themselves and subsequently blackmailing victims with the public release of said images. Prosecutions of sextortion cases involving minors have increased almost two-fold in the past 5 years. The purpose of this review is to explore the literature regarding the behavior of both victims and perpetrators, the effects on victims, and the support resources available for clinicians, victims, and parents. Recent findings Sextortion begins as an unassuming request for personal pictures and quickly escalates. Minors targeted by predators fear both punishment by guardians and the social consequences that follow the release of their explicit pictures. This cycle of victimization endangers minors and may lead to mental health problems, such as anxiety and depression. Recently, sextortion cases have risen to the forefront of national attention through the mainstream media with celebrities revealed as both perpetrators and victims. This higher visibility of sextortion highlights the importance of reviewing recent research regarding minors and their online behavior and the tactics of perpetrators. Summary Sextortion, an extreme form of cyber abuse, endangers minors and may lead to anxiety and depression. Pediatricians should be familiar with the concept of sextortion and discuss its dangers and available resources with parents and minors.
Quantum speed limit time for correlated quantum channel

Memory effects play a fundamental role in the dynamics of open quantum systems. There exist two different views on memory for quantum noises. In the first view, the quantum channel has memory when there exist correlations between successive uses of the channels on a sequence of quantum systems. These types of channels are also known as correlated quantum channels. In the second view, memory effects result from correlations which are created during the quantum evolution. In this work, we will consider the first view and study the quantum speed limit time for a correlated quantum channel. Quantum speed limit time is the bound on the minimal time which is needed for a quantum system to evolve from an initial state to desired states. The quantum evolution is fast if the quantum speed limit time is short. In this work, we will study the quantum speed limit time for some correlated unital and correlated non-unital channels. As an example for unital channels, we choose correlated dephasing colored noise. We also consider the correlated amplitude damping and correlated squeezed generalized amplitude damping channels as the examples for non-unital channels. It will be shown that the quantum speed limit time for correlated pure dephasing colored noise is increased by increasing correlation strength, while for correlated amplitude damping and correlated squeezed generalized amplitude damping channels quantum speed limit time is decreased by increasing correlation strength.
Automatic construction of multi-faceted user profiles using text clustering and its application to expert recommendation and filtering problems

In the information age we are living in today, not only are we interested in accessing multimedia objects such as documents, videos, etc. but also in searching for professional experts, people or celebrities, possibly for professional needs or just for fun. Information access systems need to be able to extract and exploit various sources of information (usually in text format) about such individuals, and to represent them in a suitable way usually in the form of a profile. In this article, we tackle the problems of profile-based expert recommendation and document filtering from a machine learning perspective by clustering expert textual sources to build profiles and capture the different hidden topics in which the experts are interested. The experts will then be represented by means of multi-faceted profiles. Our experiments show that this is a valid technique to improve the performance of expert finding and document filtering.
RetailNet: A Deep Learning Approach for People Counting and Hot Spots Detection in Retail Stores

Customer behavior analysis is an essential issue for retailers, allowing for optimized store performance, enhanced customer experience, reduced operational costs, and consequently higher profitability. Nevertheless, not much attention has been given to computer vision approaches to automatically extract relevant information from images that could be of great value to retailers. In this paper, we present a low-cost deep learning approach to estimate the number of people in retail stores in real-time and to detect and visualize hot spots. For this purpose, only an inexpensive RGB camera, such as a surveillance camera, is required. To solve the people counting problem, we employ a supervised learning approach based on a Convolutional Neural Network (CNN) regression model. We also present a four channel image representation named RGBP image, composed of the conventional RGB image and an extra binary image P representing whether there is a visible person in each pixel of the image. To extract the latter information, we developed a foreground/background detection method that considers the peculiarities of people behavior in retail stores. The P image is also exploited to detect the hot spots of the store, which can later be visually analyzed. Several experiments were conducted to validate, evaluate and compare our approach using a dataset comprised of videos that were collected from a surveillance camera placed in a real shoe retail store. Results revealed that our approach is sufficiently robust to be used in real world situations and outperforms straightforward CNN approaches.
A texture fused superpixel algorithm for coal mine waste rock image segmentation

In coal production, it is necessary to separate waste rock from raw coal. With the development of computer vision technology, the image recognition method with simple equipment and high efficiency has become a research hotspot. Image segmentation is a very important step before the image analysis in image recognition method. Traditional image segmentation takes pixel as processing unit, without considering the influence of space and texture; therefore, it is difficult to segment the coal and waste rock images when the target and the background are with similar color and the existence of weak edges and fuzzy regions. In this paper, a new superpixel segmentation algorithm that combines color, spatial position, and texture in clustering is proposed, which integrates texture information into SLIC algorithm. After test of a great number of coal and waste rock images, the segmentation results of SLIC and SLFTIC algorithms were evaluated that SLFTIC can better fit the edge. According to the evaluation index of superpixel algorithm, undersegmentation error and boundary recall of SLFTIC are superior than SLIC, and compactness score is slightly improved. For the images with similar foreground and background and complex texture, such as mineral images and medical images, this kind of superpixel cutting method has better segmentation effect.
Reinforcement Learning Based Navigation with Semantic Knowledge of Indoor Environments

Recent years have been witnessing a huge step of artificial intelligence towards being applied in autonomous robots. To build intelligent robots navigating in indoor environment, many research focus on deep reinforcement learning which help robot learn and plan by themselves. Different network architectures are proposed for training agents to navigate and find targeted objects in both real and simulated environments. Despite promising results, one key challenge remaining is that the agent has to perform well in unseen environments and objects. To solve this generalization problem, this work proposes a method using prior knowledge graph capturing relationships between target objects. Experiments on simulated environments show that not only the proposed method enhances the learning process but also significantly improves agents generalization. When compared to similar methods, proposed method has a competitive and even better performance while bringing computational advantages.