Reviewerly/exHarmony huggingface dataset:
Estimating cover image for universal payload region detection in stego images

With the increase in digital communication, images have become indispensable media for carrying obscured communication these days. Unraveling the presence or details of obscured transmission also known as steganography is termed as steganalysis. Passive steganalysis focuses only on classification of object as clean or adulterated. Finding the length of payload and regions where the message is hidden, in a stego object is a challenging task and forms a part of Active or Forensic Steganalysis. Many researchers have worked towards finding the length of message, but not much work has been done on finding region where message is hidden. This motivated us to work towards finding the region of payload in stego images. Hence this paper proposes a novel technique for detection of payload regions in a stego image combining Denoising Autoencoder with Local Binary Pattern (LBP) Operator. Denoising Autoencoder is used to learn inter relationships between pixels of an image for estimating cover image from the given stego image. LBP operator capable of capturing local inter-relationships within neighborhood coordinates, have been used to compare corresponding blocks in input stego image and estimated cover image for unraveling payload locations. The major advantage of the proposed technique is that it is universal and it does not require cover image or knowledge about steganographic algorithm used. Accuracy of 96.5% and 97.7% respectively was achieved in the experimental study conducted with spatial and JPEG domain images from BOSSBase dataset.
Autonomous Braking Algorithm for Rear-End Collision via Communication-Efficient Federated Learning

Realizing driving safety is the fundamental goal pursued by artificial intelligence (AI)-enabled autonomous driving. However, due to the limited capacity of vehicles and the limited scenarios involved, the reliability and environmental adaptability of the current single-vehicle intelligence still need to be improved. In addition, driving knowledge only exists locally in each connected autonomous vehicle (CAV) and cannot be effectively reused or shared with other CAVs. To solve the above problems, this paper proposes to use federated learning (FL) to realize the collaboration of CAVs without revealing local data, thereby improving the accuracy of CAVs decision-making and driving safety. First, for the typical rear-end collision scenario, we propose a local decision-making model that comprehensively considers multiple influencing factors to fit the actual traffic environment. Then, we design a federated learning process for knowledge sharing. In particular, we propose a model similarity method to select high-quality local models for upload, thereby reducing communication overhead while improving the accuracy of the global model. Extensive simulations validate the performance of our proposal in reducing communication overhead and improving decision accuracy.
From Data Analysis to Intent-based Recommendation: an Industrial Case Study in the Video Domain

This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the next-item recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for session-aware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.
Syntactic view of sigma-tau generation of permutations

We give a syntactic view of the Sawada-Williams (σ,τ)-generation of permutations. The corresponding sequence Seqn of στ-operations of length n!−1 is shown to have a compact description of size Θ(n2) in terms of straight-line programs. Using the compact description, we design almost linear time ranking and unranking algorithms.
Distributed Matrix Weighted Fusion Gaussian Filter

This letter is concerned with the distributed fusion estimation problem for nonlinear systems. Generally, optimal distributed weighted fusion criterion can be given by the form of analytic solution under the case of linear systems. However, this classical fusion criterion is not applicable to nonlinear systems because the error cross-covariances among local filters cannot be obtained. In this case, the statistical linear regression (SLR) method is firstly proposed to approximate the error cross-covariances among local Gaussian filters. To further reduce the performance loss caused by linearization errors in SLR, a measurement-dependent maximum likelihood criterion is developed to adjust the error cross-covariance matrices. Finally, a mobile robot localization example is employed to show the effectiveness and advantages of the proposed methods.
GROWL: Group Detection With Link Prediction

Interaction group detection has been previously addressed with bottom-up approaches which relied on the position and orientation information of individuals. These approaches were primarily based on pairwise affinity matrices and were limited to static, third-person views. This problem can greatly benefit from a holistic approach based on Graph Neural Networks (GNNs) beyond pairwise relationships, due to the inherent spatial configuration that exists between individuals who form interaction groups. Our proposed method, GROup detection With Link prediction (GROWL), demonstrates the effectiveness of a GNN based approach. GROWL predicts the link between two individuals by generating a feature embedding based on their neighbourhood in the graph and determines whether they are connected with a shallow binary classification method such as Multi-layer Perceptrons (MLPs). We test our method against other state-of-the-art group detection approaches on both a third-person view dataset and a robocentric (i.e., egocentric) dataset. In addition, we propose a multimodal approach based on RGB and depth data to calculate a representation GROWL can utilise as input. Our results show that a GNN based approach can significantly improve accuracy across different camera views, i.e., third-person and egocentric views.
Improved Single Haze Removal Algorithm Based on Color Attenuation Prior

With continuous development and application of computer vision, images with high clarity and quality are increasingly important in training different kinds of models, especially in the field of automated driving and road safety monitoring. In this paper, through learning on the benchmarking dataset, we propose a novel quadratic model of color attenuation prior to calculate the scene depth map and then obtain the transmission matrix and restore the hazy-free image. Numerous experimental results show that the proposed approach is more accurate on sculpt the correlation between scene brightness, saturation and depth, therefore it outperforms the linear model in terms of both dehazing effect and clarity. Meanwhile, the objective and subjective comparison with the other state-of-the-art algorithms on the hazy images demonstrate the efficiency and effectiveness of the proposed method.
Adaptive Online Opponent Game Policy Modeling with Association Rule Mining

Opponent modeling is a research aspect that needs close attention when facing an opponent in a complex game environment. Real-Time Strategy (RTS) games are a representative of one of the highest complex game environments. In RTS games, the players' tactical and strategical decisions need constant adaptation in order to win the game. In this work, a preliminary study is reported on opponent modeling in RTS games via the incorporation of association rule mining to identify game policies in online mode. Such insight into opponents' mode of operation should provide a player with vital information regarding choosing advantageous gameplay countermeasures against an opponent.
GDST: Global Distillation Self-Training for Semi-Supervised Federated Learning

Federated Learning (FL) refers to the machine learning scheme that enables decentralized model training over massive separate data sources without privacy concerns. However, existing works rarely consider difficulty of obtaining sufficient data labels due to uncontrollable user behavior, especially in cross-device FL scenarios. In this paper, we consider semi-supervised federated learning (SSFL) setups and mainly focus on the disjoint scenario where local clients only have access to unlabeled data. By integrating self-training scheme for unlabeled data, we propose self-training loss as part of local training objective within federated learning framework. To further stablize and improve the learning process, we propose global distillation loss that utilize output logits of global model for per client-sample as supervision and also soften such distillation by temperature to obtain more discriminative information. Based on self-training and global distillation loss, combined with server-side training, we propose Global Distillation Self-Training (GDST) Federated Learning algorithm, which enables to distributedly learn a global model in the disjoint scenario of SSFL. Finally, we do sufficient ablation study to explore the role of each component of our GDST method, experimentally guarantee the interpretability.
Named Entity Recognition applied on Moroccan tourism corpus

The tourism industry has particularly evolved in the era of social media, and due to the huge amount of data available on the web, tourism forums have become spaces for exchanging information about many topics concerning a country’s tourism and hospitality, which has helped to establish a bank of knowledge that might encourage artificial intelligence researchers to follow some research tracks that could lead to interesting insights for decision makers in the sphere of tourism. This paper is an application of artificial intelligence within the tourism industry in the context of Morocco, whereby we carried out natural language processing transformers to classify entities (i.e., Named Entity Recognition) in text that was collected from the Moroccan forum in TripAdvisor.
Side-Channeling the Kalyna Key Expansion

In 2015, the block cipher Kalyna has been approved as the new encryption standard of Ukraine. The cipher is a substitution-permutation network, whose design is based on AES, but includes several different features. Most notably, the key expansion in Kalyna is designed to resist recovering the master key from the round keys. In this paper we present a cache attack on the Kalyna key expansion algorithm. Our attack observes the cache access pattern during key expansion, and uses the obtained information together with one round key to completely recover the master key. We analyze all five parameter sets of Kalyna. Our attack significantly reduces the attack cost and is practical for the Kalyna-128/128 variant, where it is successful for over 97% of the keys and has a complexity of only $$2^{43.58}$$ . To the best of our knowledge, this is the first attack on the Kalyna key expansion algorithm. To show that the attack is feasible, we run the cache attack on the reference implementation of Kalyna-128/128, demonstrating that we can obtain the required side-channel information. We further perform the key-recovery step on our university's high-performance compute cluster. We find the correct key within 37 hours and note that the attack requires 50K CPU hours for enumerating all key candidates. As a secondary contribution we observe that the additive key whitening used in Kalyna facilitates first round cache attacks. Specifically, we design an attack that can recover the full first round key with only seven adaptively chosen plaintexts.
Multitarget Detection in Depth-Perception Traffic Scenarios

Multitarget detection in complex traffic scenarios usually has many problems: missed detection of targets, difficult detection of small targets, etc. In order to solve these problems, this paper proposes a two-step detection model of depth-perception traffic scenarios to improve detection accuracy, mainly for three categories of frequently occurring targets: vehicles, person, and traffic signs. The first step is to use the optimized convolutional neural network (CNN) model to identify the existence of small targets, positioning them with candidate box. The second step is to obtain classification, location, and pixel-level segmentation of multitarget by using mask R-CNN based on the results of the first step. Without significantly reducing the detection speed, the two-step detection model can effectively improve the detection accuracy of complex traffic scenes containing multiple targets, especially small targets. In the actual testing dataset, compared with mask R-CNN, the mean average detection accuracy of multiple targets increased by 4.01% and the average precision of small targets has increased by 5.8%.
Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems

Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an Adaptive Guided Spatial Compressive CS (AGSCCS) has been proposed to handle the weaknesses of CS. Firstly, we adopt a chaotic mapping method to generate the initial population in order to make it more uniform. Secondly, a scheme for updating the personalized adaptive guided local location areas has been proposed to enhance the local search exploitation and convergence speed. It uses the parent’s optimal and worst group solutions to guide the next iteration. Finally, a novel spatial compression (SC) method is applied to the algorithm to accelerate the speed of iteration. It compresses the convergence space at an appropriate time, which is aimed at improving the shrinkage speed of the algorithm. AGSCCS has been examined on a suite from CEC2014 and compared with the traditional CS, as well as its four latest variants. Then the parameter identification and optimization of the photovoltaic (PV) model are applied to examine the capacity of AGSCCS. This is conducted to verify the effectiveness of AGSCCS for industrial problem application.
The Application of Blockchain as a Distributed Ledger and Smart Contract for Property Registration

Most of the current systems in the world work under centralized architecture, blockchain provides a decentralized solution for software development. Though centralization has many benefits such as regulation, control, privacy, quick decision making and implementation [1] it no longer is a viable solution for the ever-growing technology industry. Though systems that are decentralized exist such as torrent they have not been able to replace the centralized system due to the lack of authentication and security. Blockchain is a solution that mitigates those risks as it provides security and authentication for all the participants of the network.[2] The current property registration for residential and commercial developments is a centralized system where majority of the work is done on a pen and paper-based system. The current system involves a lot of vulnerabilities and people use it to cheat others and the government. Blockchain is used to provide a potential solution for many challenges of land registration. The system involves noting down transactions into a hard copy ledger which is the only record that a transaction has occurred. We aim to evaluate the current registration system and assess the suitability of blockchain to replace the current centralized system. Most developing countries don't have a proper working system of tracking the property rights, the one they have is too fragile. For instance, a large earthquake can destroy the municipal buildings that store documents regarding the ownership of the properties.
Securing IoT Transactions Against Double-Spending Attacks based on Signaling Game Approach

With considerable demand for higher throughput, greater capacity, and lower latency for consumers, the Internet of Things (IoT) network is anticipated to meet the desired security and privacy requirements. This study provides high transaction throughput on critical IoT applications, particularly Bitcoin security against double-spending attacks. To this end, we investigated the signaling game approach to model the interaction between two miners while considering players behavior (malicious or honest miners) and the incoming transaction throughput. To the best of our knowledge, this is the first work that exploits the signaling game to cover the incoming transactions randomness waiting for validation, which influences the honest miners behavior. With extensive simulations, we show that our proposed signaling game reduces the impact of double-spending attacks on IoT transactions. The results also illustrate the benefit of using the signaling game to model the interaction between two miners while handling the incomplete information of the incoming transactions and the type of miners.
TeNDER: towards efficient Health Systems through e-Health platforms employing multimodal monitoring

There is a huge challenge to reach efficiency of national health systems and Information and Communication Technologies (ICTs) play a significant role towards such objective. The increasing connectivity and the fast development and availability of imaging and biometric sensors as well as of the Internet of Things devices have opened a world of possibilities. One of these examples is given by the automatic distant monitoring of Parkinson&#x0027;s and Alzheimer&#x0027;s patients by the collection of data that could be analyzed to reveal valuable insights for early detection and/or prevention of events related to their condition. In this paper, a complete overview of a system intended to improve the Quality-of-Life (QoL) of such patients is presented. The system collects signals from diverse sensors, identifies the user behavior and context, and triggers proper actions for improving the patient&#x0027;s QoL. The system offers comparable/improved results for the detection of abnormal behavior in daily motion with respect to the state-of-the-art.
Diagnosis of Aglaonema Plant Disease Using Forward Chaining and Naive Bayes Methods

The aglaonema plant is quite in demand by the public because it is a current trend and can be a business field for ornamental plant lovers. However, the growth of aglaonema plants can be hampered by disease, so a system is needed. This Aglaonema plant disease diagnosis expert system was created to determine the type of disease so that it can be overcome as early as possible. The expert system method used is the Forward Chaining method and the Naive Bayes method. The Forward Chaining method is used to make rules based on symptoms and the Naïve Bayes method to determine the results of the diagnosis. This study used 25 symptoms for 10 diseases and obtained an accuracy value of 76% using 50 data.
Using Virtual Reality to Improve Classroom Behavior in People With Down Syndrome: Within-Subjects Experimental Design

People with Down syndrome face various learning challenges. Introducing new and enjoyable experiences in learning settings may improve learning outcomes. Immersive and interactive technologies such as virtual reality can be used to deliver rich visual experiences in classrooms.The aim of this study was to investigate the feasibility and benefits of virtual reality exposure for people with Down syndrome in learning settings.To address this aim, we used a within-subjects design to assess the effect of a brief virtual reality drawing experience and conventional drawing experience on subsequent behavior in 16 participants.Large positive effects were found for virtual reality drawing (t15=5.020, P<.001) and conventional drawing (t15=3.720, P=.002) in improving subsequent behavior in a learning setting. Irrespective of the intervention, the participant's mood, attention, and overall behavior significantly improved. No significant differences were found between the interventions (t15=-0.648; P=.53).This study's results are encouraging for researchers and educators interested in using virtual reality for people with Down syndrome, as virtual reality was found to be highly feasible. Recommendations are made for researchers and educators interested in providing virtual reality experiences for people with Down syndrome.
The Unrepeatable Human Mind—Challenges in the Development of Cognitive Information Systems—What Makes a Machine Human?

The understanding of human cognition has not been fully achieved; therefore, Information Systems (IS) are not yet fully synchronized with humans. By understanding the cognition process, we will be able to create a human-tailored Cognitive Information Systems (CISs). The necessity for this research is supported by the fact that present business decision makers are faced with challenges that they cannot solve in the time available without CIS. Here, the aim of the authors is to underpin the adaptability of cognitive resonance and the role of the info-communication via Human–Computer Interaction (HCI)—including linkage, relation and impacts, showing the needed direction to increase the effectiveness of the Human–Computer Interaction (HCI), that leads to an improved CIS building with a higher cognitive level. The applied research methodology consists of research analyses and an assessment of the available publications to pursue a comparative study pattern; then, a model building paradigm has been used for observing and monitoring the work with a CIS during HCI. We found a huge gap regarding information processing in the recent literature, that has been caused by the wide range of interdisciplinarity. Our research approach provides an overview of how other disciplines influence HCI and how the human mental model is supported with value added.
Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axis.