Reviewerly/exHarmony huggingface dataset:
JPEG Steganalysis Based on Local Dimension Estimation

In this paper, a novel JPEG steganalysis scheme using local dimension estimation is proposed. Most steganalysis methods are based on the Cachin's statistical law. However, the high dimensionality of the feature space as well as the lack of the universal image statistical models limits the current steganalysis. The proposed method regards the difference between the host and stego images as a manifold and estimates its local dimension. By comparing the local dimensions of various data hiding methods, the proposed scheme can decide both the data-hiding method and the embedding rate. Experimental results demonstrate the effectiveness of the algorithm.
Adaptive Nonlinear PID Control Using Output Recurrent Broad Learning System for Discrete-Time Nonlinear Dynamic Systems

This paper presents a new learning control structure using output recurrent broad learning system (ORBLS) for adaptive nonlinear PID control of unknown digital nonlinear dynamic systems with time delays. The proposed control method, abbreviated as ORBLS-ANPID, is novel in combining ORBLS and model predictive control to develop a new Nonlinear PID control law for high-performance setpoint tracking control and disturbance rejection. Comparative simulations on two renowned nonlinear digital time-delay systems are well used to show the effectiveness and superiority of the proposed method by comparing to six existing methods.
Convolutional Neural Networks-Based Sentence Level Classification of Crime Documents

Das, Priyanka Das, Asit KumarThe proposed work demonstrates a sentence level classification scheme for crime documents based on Convolutional Neural Networks (CNN). Unlike other existing approaches, deep convolutional neural networks give importance to the semantic meaning of the text documents, and therefore, are more reliable for classification purposes. The sentences of the reports are also known as phrases which have been vectorized using GloVe. The vectorized phrases have been fed to the CNN-based classification system. The dataset used in the proposed work provides a broad categorization of crime information. But this broad categorization resulted in skewed data that eventually caused potential difficulties while obtaining accuracy on the test data. Therefore, this data imbalance problem has been surpassed by combining the similar classes which reduced the total number of classes. Thus the dataset has been balanced and crime reports have been classified. The effectiveness of the proposed methodology has been evaluated by comparing it with other existing methods.
Multi-Label Streaming Feature Selection via Class-Imbalance Aware Rough Set

Multi-label feature selection aims to select discriminative attributes in multi-label scenario, but most of existing multi-label feature selection methods fail to consider streaming features, i.e. features gradually flow one by one, which is more common in real-world applications. In addition, though there are already some representative works on multi-label streaming feature selection, they fail to tackle the class-imbalance problem, which exists widely in multi-label learning. In fact, class-imbalance will lead to the performance degradation of multi-label learning models. Thus considering class-imbalance problem in multi-label scenario is beneficial to multi-label feature selection because more precise feature evaluation is achieved. Motivated by this, we propose a new rough set named as class-imbalance aware rough set model which can fit class-imbalance problem well. To address streaming features, we construct a novel streaming feature selection framework called SFSCI(Streaming Feature Selection via Class-Imbalance aware rough set), which contains online irrelevancy discarding and online redundancy reduction. Finally, an empirical study on a series of benchmark data sets demonstrates that the proposed method is superior to other state-of-the-art multi-label feature selection methods, including several multi-label streaming feature selection methods.
PC-PINet: Partial Re-identification Network for People Counting with Overlapping Cameras

People counting is one of the important tasks in intelligent video surveillance. However, reliable people counting in the overlapping areas under multiple cameras is still a challenging task due to the occlusion, small object, and minor individual similarity. In this paper, we propose a novel partial body detector to detect the upper bodies of the people in the images captured by two cameras, and our proposed partial body detector can achieve the balance between the speed and the detection accuracy. Furthermore, we propose a Convolutional Neural Network based person re-identification framework, namely Partial Reidentification Network (PINet), which make full use of the multilevel features and the identity information. To demonstrate the effectiveness of our propose method, we collect classroom datasets and conduct extensive experiments on the dataset. The experimental results show that our proposed method achieves excellent performance on the classroom dataset.
Advances in Password Recovery Using Generative Deep Learning Techniques

Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates. In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-based deep neural networks, autoencoding mechanisms and generative adversarial networks. We provide novel generative deep-learning models in terms of variational autoencoders exhibiting state-of-art sampling performance, yielding additional latent-space features such as interpolations and targeted sampling. Lastly, we perform a thorough empirical analysis in a unified controlled framework over well-known datasets (RockYou, LinkedIn, MySpace, Youku, Zomato, Pwnd). Our results not only identify the most promising schemes driven by deep neural networks, but also illustrate the strengths of each approach in terms of generation variability and sample uniqueness.
Typing on Tap: Estimating a Finger-Worn One-Handed Chording Keyboard’s Text Entry Rate

The Tap StrapTM enables eyes-free mobile typing using a one-hand device worn on the user’s fingers. Using the standard MacKenzie-Soukoreff text entry phrase set, 12 participants completed 480 minutes of practice with the keyboard on either their dominant or non-dominant hand. Average final typing rate was 22.11 words per minute (WPM), and letter accuracy increased from 85.11% to 91.02%. Non-dominant hand users typed more accurately than dominant after 420 minutes of practice.
Student Attendance Control System with Face Recognition Based on Neural Network

In this research we implement a system to monitor student attendance at South Ural State University. Our system is based on the automatic recognition of students' faces using neural networks. In this paper, we investigate the process, methods and algorithms of face recognition to build a student attendance control system that allows to easily add a new person to the system without additional training of neural networks within the system. In working, we identified and studied the parameters that reduce the efficiency of the recognition, as well as equipping our system with algorithms that reduce the negative impact of these parameters. We conducted experiments that prove the effectiveness of our solution.
Research on Human Abnormal Behavior Detection Based on Deep Learning

In order to make full use of the effective information in the video and improve the recognition rate of abnormal human behavior in complex scenes, we use a mixed Gaussian model to detect clear foreground moving target contours and perform Gaussian filtering on them to remove the effects of noise in the scene . By calculating the center point of the foreground pixel, and drawing a bounding box based on this, the key area of human motion in the video is extracted. Then we use the Farneback dense optical flow algorithm to obtain spatiotemporal information. By combining CNN and LSTM, a CNN-LSTM hybrid two-stream network model based on the Dropout mechanism is established., input the original image and the superimposed optical flow image of the key area of the video sequence motion to learn the dynamic and static features and timing information in the spatiotemporal information. The weighted fusion method is used to perform weighted calculation on the Softmax output of the two-way network to obtain results. The tresults show that the accuracy of the behavior classification reached 91.2%, and the recognition rate of abnormal behavior was 92%. Compared with the three models in the article, the improvement was 6% 8.3%, 3.4%.
Inspection and Verification Training System of Production Lines in Automated Processes, Through Virtual Environments

This work presents the development of a training system in an interactive virtual environment, for the inspection and verification of a bottled bottle, in the final production line of a bottling plant, through the use of an artificial vision camera applied in a plant. Virtual, which presents the emulation of the quality control process in an industry. In addition, the system allows training within an industrial plant for the detection of faults such as the amount of liquid, lid alignment and label placement, in commercially manufactured products, and mainly in the development of skills for the configuration of a machine vision camera.The training system has been created using a CAD tool and the Unity3D graphics engine, which provide a high level of realism, offering immersion and interaction between the components of the environment and the user.
Fast path planning for underwater robots by combining goal-biased Gaussian sampling with focused optimal search

Autonomous path planning plays an important role in the navigation of intelligent underwater robots. Path planning is a nondeterministic polynomial hard issue in classical path planning models. This problem can be solved using various sample-based strategies. However, the effectiveness of these sample-based strategies is significantly lower in underwater environments, owing to the special undulating terrain and obstacles that are sparser compared to those in the ground. In this study, a more efficient underwater path planning method is proposed for underwater robot navigation. The method employs a goal-biased Gaussian sampling algorithm to select searching nodes optimally, and a focused optimal search algorithm is proposed to accelerate the path optimization process. Combining these two algorithms results in high-efficiency and fast autonomous underwater path planning. Experimental results demonstrate that our method can generate a shorter path and is more efficient than a rapidly exploring random tree star in underwater robot navigation.
A visual self-attention network for facial expression recognition

In real world, facial expression images are easily disturbed by different external environments, such as face poses, illumination intensities, and occlusion, and features extracted from whole facial expression images often have a lot of noise, which leads to low recognition accuracy of the model. Inspired by the facts that local images are less disturbed by the environment and human beings usually recognize facial expressions by integrating some local information about human faces, this paper proposes a model for recognition of facial expressions, based on the fusion of local image features, that aims to recognize facial expressions by comprehensively considering local information about human faces. To particularly integrate the robust characteristics of local images, this paper constructs a visual self-attention network by transformation of the Transformer model in the field of natural language processing, which can take into account the interaction between local images and make the image feature more discriminative. To demonstrate the performance of the model, this paper conducts experiments on three constrained small datasets, CK+, Oulu-CASIA and MMI, and three unconstrained large datasets, FER2013, RAF-DB and Expw. The results show that the proposed model can attain the recognition accuracy of the state-of-the-art methods on all datasets, thereby verifying the effectiveness of the proposed method.
Sequence Adaptation via Reinforcement Learning in Recommender Systems

Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item interactions is required as input to train the models. This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations. Hence, baseline strategies might ignore important sequential interactions or add noise to the models with redundant interactions, depending on the variety of users’ sequential behaviours. To overcome this problem, in this study we propose the SAR model, which not only learns the sequential patterns but also adjusts the sequence length of user-item interactions in a personalized manner. We first design an actor-critic framework, where the RL agent tries to compute the optimal sequence length as an action, given the user’s state representation at a certain time step. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network, while at the same time we adapt the sequence length with the actor network in a personalized manner. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches. Finally, we make our implementation publicly available at https://github.com/stefanosantaris/sar.
Automated Graph Representation Learning for Node Classification

Graphs are ubiquitous and play an essential role in the real world. A vital prerequisite for analyzing graphs is to learn their effective representations. Most existing graph representation learning models are hand-crafted, which lack the scalability to different kinds of graphs. In this paper, we present AutoGRL, an AUTOmated Graph Representation Learning Framework for node classification. We first design an appropriate search space with four critical components in the automated machine learning (AutoML) pipeline: data augmentation, feature engineering, hyper-parameter optimization, and neural architecture search. Then we search for the best graph representation learning model in the search space on given graph data using an efficient searching algorithm. We conduct extensive experiments on four real-world node classification datasets to demonstrate that AutoG Rlcan automatically find competitive graph representation learning models on specific graph data effectively and efficiently.
Design of Deep CNN Model for Effective Traffic Signs Recognition

In this paper, the method to design deep Convolutional Neural Network (CNN) architecture for the problem of traffic signs classification is proposed. The approach incorporates five main stages followed by each other: selecting the deepness of the network via the number of convolutional-pooling pairs, choosing the needed number of feature maps in every convolutional layer, identifying the sufficient number of neurons in the hidden fully connected layer, estimating the needed percentage of dropout after every layer. The last stage is aimed to analyse if replacing max pooling by convolution with strides 2 or by average pooling improves designed architecture. At each stage, every designed model is trained and evaluated 100 times. The choice of the best model to continue with at the next stage is based on the highest of the most frequent accuracy. The obtained final architecture is trained and tested with the original German Traffic Sign Recognition Benchmark (GTSRB) and its augmented version via geometric transformations: rotation around the centre point and projection to different sides that imitates capturing of images by the camera with some skew angle. Experimental results showed testing accuracy of more than 99.9% that enables to use designed architecture in real-life applications.
IoT device for Athlete's movements recognition using inertial measurement unit (IMU)

In modern sports training, collection, acquisition, and analysis of data concerning the athlete's activities are a necessity to improve trainings. This paper presents a new wearable IoT device, capable of monitoring athletes' movements in real time. The collected data are of a great importance for training sessions and improvement of athlete's performances. The proposed wearable device located on the athletes' wrist is an inertial sensing module that connects to Wi-Fi and sends information about Athlete activities. This paper presents also an electrical design proposed for such connected device using an inertial measurement unit (IMU) as a sensor. Furthermore, this article investigates different IoT communication technologies and protocol used for securing data transmission. An application was developed for data visualization and for activities classification. The application allows to remotely view the athletes' movement corresponding signals and detect the associated activity. The experimental results of this research show that the developed system is able to identify several activities of players during their training with a high success rate.
Classification of Stages of Thyroid Nodules in Ultrasound Images Using Transfer Learning Methods

Early detection of any disease assists in reduction of disease progression. In this work, discrimination of the thyroid nodules is studied by employing deep learning architectures. Deep learning algorithm mimics the function of the human cerebral cortex. Thyroid nodule classification is done using Convolutional Neural Network (CNN). It is observed that early detection of any disease will help to reduce the risk of death. In CNN, the output of the input layer is fed the convolutional layer followed by ReLU layer and Max pooling layer and the images are classified into Benign (TI-RADS 2, TI-RADS 3, TI-RADS 4a) and Malignant (TI-RADS 4b, TI-RADS 5, TI-RADS 6). The performance of CNN is compared with the pre-trained networks such as Alexnet, VGG-19 and Resnet-50 using transfer learning. The CNN outperformed the pre-trained networks with an accuracy of 99.17%, sensitivity of 0.98, precision of 0.97 and F1 score of 1.
Technical Debt predictive model through Temporal Convolutional Network

Technical debt is a metaphor that refers to all the consequences of poorly written code and trade-offs in development. Early technical debt diagnosis is important for software developers because it allows planning for software maintenance and improvement activities, such as refactoring, to prevent system degradation. Several studies have been conducted in the literature on the identification of the technical debt and its consequences, thanks to useful tools for identifying the problem within the source code. On the other hand, this work aims to explore a deep learning approach to predict the rise of technical debt in software code by leveraging the knowledge of changing quality metrics. For validation of the approach, a large dataset was built, related to four known Java software projects, with the collection of numerous class-level code quality metrics. The results obtained show the effectiveness of the proposed approach in predicting the development of Technical Debt within the source code. We obtained an F1 score of 0.99 for two of the chosen software systems and greater than 0.91 for the remaining two.
StegoPass – Utilization of Steganography to Produce a Novel Unbreakable Biometric Based Password Authentication Scheme

In the digital era we live, trustworthy verification schemes are required to ensure security and to authenticate the identity of an individual. Traditional passwords were proved to be highly vulnerable to attacks and the need of adopting new verification schemes is compulsory. Biometric factors have gained a lot of interest during the last years due to their uniqueness, ease of use, user convenience, and ease of deployment. However, recent research showed that even this unique authentication factors are not inviolable techniques. Thus, it is necessary to employ new verification schemes that cannot be replicated or stolen. In this paper we propose the utilization of steganography as a tool to provide unbreakable passwords. More specifically, we obtain a biometric feature of a user and embed it as a hidden message in an image. This image is then utilized as a password, the so-called StegoPass. Reversely, when a legit user or an attacker tries to unlock a device or an application, the same biometric feature is captured and embedded with the same steganography algorithm into the picture. The hash key of the resulted stego image in both cases is produced and if there is a complete match, user is considered as authenticated. To ensure that the proposed StegoPass cannot be replicated, we have conducted experiments with state-of-the-art deep learning algorithms. Moreover, it was examined whether Generative Adversarial Networks could produce exact copies of the StegoPass to fool the suggested method and the results showed that the proposed verification scheme is extremely secure.
Checking Robustness of Representations Learned by Deep Neural Networks

Recent works have shown the vulnerability of deep neural networks to adversarial or out-of-distribution examples. This weakness may come from the fact that training deep models often leads to extracting spurious correlations between image classes and some characteristics of images used for training. As demonstrated, popular, ready-to-use models like the ResNet or the EfficientNet may rely on the non-obvious and counterintuitive features. Detection of these weaknesses is often difficult as classification accuracy is excellent and does not indicate that the model is non-robust. To address this problem, we propose a new method and a measure called robustness score. The method allows indicating which classes are recognized by the deep model using non-robust representations, i.e. representations based on spurious correlations. Since the root of this problem lies in the quality of the training data, our method allows us to analyze the training dataset in terms of the existence of these non-obvious spurious correlations. This knowledge can be used to attack the model by finding adversarial images. Consequently, our method can expose threats to the model's reliability, which should be addressed to increase the certainty of classification decisions. The method was verified using the ImageNet and Pascal VOC datasets, revealing many flaws that affect the final quality of deep models trained on these datasets.