Reviewerly/exHarmony huggingface dataset:
Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms

The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.
A deep learning network based end-to-end image composition

Currently, high-quality image composition largely depends on multiple user interactions and complex manual operations. In particular, the process of composition object extraction and region determination has become a burden that cannot be underestimated, restricting wider applications. Aiming at this problem, we propose an end-to-end image composition method that combines powerful deep-learning-based application modules such as image retrieval and instance segmentation to realize efficient non-interactive image composition. Specifically, the retrieval module, which is based on the attention mechanism, can determine semantically similar material images. Moreover, the content of interest (COI) extraction and optimization procedure is able to select the most proper instance among the material images. Finally, we propose the double-sieving strategy, which locates the best composition position in the target image. Using these effective modules, we carried out niche targeting experiments using an image database with high plausibility. The realistic experimental results illustrate that our method can achieve effective and reasonable end-to-end image composition. • We propose an image retrieval method that is based on the attention mechanism and focuses on the background of the target image, directly contributing to obtaining semantically similar images from the material images. • We propose a depth estimation-based method that can extract and optimize the COI of the material image. • We propose the double-sieving procedure to locate the composition area. • We propose an end-to-end image composition model that is free from interacting with the user and prove its efficiency via experiments.
A Model Fusion Approach for Goods Information Inspection in Dual-Platform E-Commerce Systems

In nowadays, the large-scale e-commerce corporations tend to operate their own logistics networks to guarantee the speed, safety and economic efficiency of goods delivery. Thus, an e-commerce corporation usually needs to manage an online retailing platform and a logistics platform simultaneously. Despite the offered convenience, the cross-platform management faces grand challenges on security. The inappropriate philosophies for maintaining goods information and malicious behaviors of some merchants may induce mismatch between the goods and corresponding information, which consequently leads to incorrect delivery and the degradation of customer experience. In order to tackle this issue, an innovative model fusion method is proposed in this work for goods information inspection. It investigates the advantages of multiple natural language processing models as well as domain knowledge to extract informative text features, which are subsequently fed into a multi-layer perceptron for final decision on whether the goods information is accurate. Unlike the recent popular deep architectures, the proposed method leverages the complimentary effect of features from different sources utilizing a wide structure to achieve a superior inspection accuracy. Finally, the proposed method is validated using real JD Logistics data and is demonstrated to outperform the existing techniques. Furthermore, the experimental results also demonstrate that leveraging the complimentary effects can bring additional improvement compared to merely exploring deeper.
Virtual Rotations for Maneuvering in Immersive Virtual Environments

In virtual navigation, maneuvering around an object of interest is a common task which requires simultaneous changes in both rotation and translation. In this paper, we present Anchored Jumping, a teleportation technique for maneuvering that allows the explicit specification of a new viewing direction by selecting a point of interest as part of the target specification process. A first preliminary study showed that naïve Anchored Jumping can be improved by an automatic counter rotation that preserves the user's relative orientation towards their point of interest. In our second, qualitative study, this extended technique was compared with two common approaches to specifying virtual rotations. Our results indicate that Anchored Jumping allows precise and comfortable maneuvering and is compatible with techniques that primarily support virtual exploration and search tasks. Equipped with a combination of such complementary techniques, seated users generally preferred virtual over physical rotations for indoor navigation.
Designing an LSTM and Genetic Algorithm-based Sentiment Analysis Model for COVID-19

Rani, Poonam Shokeen, Jyoti Majithia, Arjun Agarwal, Amit Bhatghare, Ashish Malhotra, JigyasuThe unleashing of Coronavirus on human lives has drastically changed a lot of things. The pandemic has been a difficult time for everybody as people lost their jobs and businesses, the economy dwindled, health issues due to the virus, be it physical or mental, were prevalent. Loneliness, depression, and anxiety caused by lockdown and work from home became the new normal. It, therefore, becomes imperative to study the large amount of social media data using computational methods and gauge the sentiment of people on various policies and strategies undertaken to fight the pandemic and take decisions accordingly. We introduce a Social Media Pandemic Sentiment Model on COVID-19 Twitter dataset to study the sentimental variation in people throughout the duration of pandemic and derive useful results out of it. We also provide an extensive comparative analysis of this model with other conventional states of the art models to display the competence of our model.
Leakage or Identification

The convenience of laptops brings with it the risk of information leakage, and conventional security systems based on the password or the explicit biometric do little to alleviate this problem. Biometric identification based on anatomical features provides far stronger security; however, a lack of suitable sensors on laptops limits the applicability of this technology. In this paper, we developed a behavior-irrelevant user identification system applicable to laptops with a metal casing. The proposed scheme, referred to as LeakPrint, is based on leakage current, wherein the system uses an earphone to capture current leaking through the body and then transmits the corresponding signal to a server for identification. The user identification is achieved via denoising, dimension reduction, and feature extraction. Compared to other biometric identification methods, the proposed system is less dependent on external hardware and more robust to environmental noise. The experiments in real-world environments demonstrated that LeakPrint can verify user identity with high accuracy (93.6%), while providing effective defense against replay attacks (96.5%) and mimicry attacks (90.9%).
Evaluation of the IP Identification Covert Channel Anomalies Using Support Vector Machine

IP Identification (IP ID) is an IP header field that identifies a data packet in the network to distinguish its fragments from others during the reassembly process. Random generated IP ID field could be used as a covert channel by embedding hidden bits within it. This paper uses the support vector machine (SVM) while enabling a features reduction procedure for investigating to what extend could the entropy feature of the IP ID covert channel affect the detection. Then, an entropy-based SVM is employed to evaluate the roles of the IP ID covert channel hidden bits on detection. Results show that, entropy is a distinct discrimination feature in classifying and detecting the IP ID covert channel with high accuracy. Additionally, it is found that each of the type, the number and the position of the hidden bits within the IP ID field has a specified influence on the IP ID covert channel detection accuracy.
Clustering of Earthquake and Volcanic Eruption Trauma Survivor Groups using K-Means Algorithm

Prolonged trauma conditions in a person have the potential to become Post-Traumatic Stress Disorder (PTSD). The American Psychological Association (APA) defines PTSD as an experience of someone who experiences a traumatic event that can cause disturbances in self-integrity, feeling of helplessness and specific trauma. People who are directly affected by earthquakes and volcanic explosions generally experience trauma. In order to reconcile the survivors of trauma, this study will categorize trauma survivors from Mount Sinabung's eruption in Karo Regency and the Ambon City Earthquake according to their level of trauma. The clustering process uses the Data Mining, a method to extract and identify trauma survivor data in order to produce the required information. The algorithm on K-Means is used in the computational process. The algorithm on K-Means has advantages in computational efficiency and ease of use. The data collection instrument in this study used the Impact of Even Scale-Revised (IES-R) Questionnaire which offered a common language and standard criteria for the classification of mental disorders. Information on the trauma survivors cluster of Mild, Moderate, and Severe trauma levels will be generated using the computational and iteration process supported by the Orange application. The results of the research on grouping trauma survivors using the K-means algorithm with the support of an application that helps the iteration process of survivor data processed through the IER-S questionnaire provide information that is useful for healing trauma survivors.
Privacy-Preserving Phrase Search over Encrypted Data

Phrase search, as a special multi-keyword search, plays an important role in many application scenarios. It not only requires the searched keywords are consecutive, but also the location relation of searched keywords is fixed. To save local storage, more and more data owners would like to outsource their data to the cloud server. In order to ensure data security and user privacy, data is generally stored on the cloud server in encrypted form. Unfortunately, encrypted data will lose the ability of being directly searched. In order to address the problem, Searchable Symmetric Encryption (SSE) technology came into being. Most of existing phrase search SSE schemes require two rounds of interaction between the user side and the cloud server or a trusted third party or disclose location information to cloud server. In this paper, we present a phrase search SSE scheme that supports dynamic update. Our scheme only needs single round interaction between the user and the cloud server, and the location information cannot be revealed. Our scheme exploits Virtual Binary Tree (VBTree), which is only a logical structure used to store keywords. In actual storage, elements of each leaf node are stored in the hash table and non-leaf node is mapped to the bloom filter. In order to determine the location relation of searched keywords, our scheme utilizes homomorphic encryption and bilinear map. In this way, the location information cannot be revealed.
Object Detection in Foggy Weather Conditions

In this paper, we addressed the problem of object detection under foggy weather in an outdoor environment. State-of-the-art object detection schemes perform very well in normal weather conditions but many of them fail when it comes to adverse weather. We proposed a novel approach to remove fog followed by object detection using PP-YOLO. Removal of fog is achieved using a U-shaped network with residual feedback from one layer to another. The network resembles an encoder-decoder scheme which enables us to reduce noise in the form of fog. Defogged images are then fed to the object detection network i.e. PP-YOLO. For the training purpose, we synthesized a dataset of foggy images and also used other existing datasets. Experimental results show the efficiency of the proposed scheme when compared to many existing methods for fog removal. Ablation studies show that the inclusion of the fog removal method certainly improves the detection performance.
Deep Learning on SDF for Classifying Brain Biomarkers

Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer’s disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Thus a less invasive and cheaper approach is demanded. MRI which has been used widely in preclinical AD has recently shown the capability to predict brain Aβ positivity. This motivates us to develop a method, SDF sparse convolution, taking MRI to predict Aβ positivity. We obtain subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and use our method to discriminate Aβ positivity. Theoretically, we provide analysis towards the understanding of what the network has learned. Empirically, it shows strong performance on par or even better than state of the art.
D<sup>2</sup>Graph: An Efficient and Unified Out-of-Core Graph Computing Model

Graph computing recently receives intensive interests due to a wide range of needs to analyse big graphs in real time. Many single-machine out-of-core graph computing systems have been developed to process large-scale graphs by utilizing effective memory access techniques. The current systems either focus on graph compression or pay attention to disk scheduling strategies. In this paper, we propose an efficient and unified out-of-core graph computing model on a single machine named D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph which combines the differential storage strategy and the dynamic caching mechanism. D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph improves the spatial locality of graph data and the temporal locality of graph computing at the same time. The main contributions of our work lie in three aspects: firstly, we design a fine-grained differential storage(FGDS) strategy to reorganize graph data on disk; secondly, we propose a dynamic caching mechanism (DCM) to selectively load the necessary edge chunks; thirdly, we combine FGDS and DCM to construct D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph and evaluate it on five public graph data sets. What’s more, to optimize the performance of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph, we also make use of the multi-threaded parallel technology. A series of results show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph outperforms state-of-the-art out-of-core graph computing systems by 1.3x-17.9x.
Trajectory Planning ROS Service for an Autonomous Agricultural Robot

In this work, we present a Robot Operating System (ROS) service implementation to generate polynomial trajectories for an Unmanned Ground Vehicle (UGV). The algorithm has been tested on a tracked robotic platform in order to follow a set of waypoints in an open-field navigation scenario, using a state-feedback controller. The odometry estimation of the ground robot leverages on GNSS readings and motor encoders. Experimental tests are analyzed and results presented.
Aggressive Growing Mixup: A Faster and Better Semi-Supervised Learning Approach

Semi-supervised learning has proven to be a powerful paradigm to avoid overfitting small labeled training sets. In this work, we propose a more robust algorithm to make better leverage unlabeled samples for generalization models compared to the latest semi-supervised learning approaches. Our method, named Aggressive Growing Mixup or AGMixup, works for mixing labeled and unlabeled samples using Mixup-the batch size of unlabeled samples is aggressively increased. We show that AGMixup achieves state-of-the-art results on standard semi-supervised learning benchmarks CIFAR-10 and SVHN. For example, AGMixup outperforms all previous approaches and achieves an error rate of 4.25% on CIFAR-10 with only 250 examples and an error rate of 2.25% on SVHN with only 4000 examples, nearly matching the performance of the fully supervised model trained with all labeled examples.
Improved Early Exiting Activation to Accelerate Edge Inference

As mobile & edge devices are getting powerful, on-device deep learning is becoming a reality. However, there are still many challenges for deep learning edge inferences, such as limited resources such as computing power, memory space, and energy. To address these challenges, model compression such as channel pruning, low rank representation, network quantization, and early exiting has been introduce to reduce the computational load of neural networks at a whole. In this paper, we propose an improved method of implementing early exiting branches on a pre-defined neural network, so that it can determine whether the input data is easy to process, therefore use less resource to execute the task. Our method starts with an entire search for activations in a given network, then inserting early exiting modules, testing those early exit branches, resulting in selecting useful branches that are both accurate and fast. Our contribution is reducing the computing time of neural networks by breaking the flow of models using execution branches. Additionally, by testing on all activations in neural network, we gain knowledge of the neural network model and insight on where to place the ideal early exit auxiliary classifier. We test on ResNet model and show reduction in real computation time on single input images.
Metaheuristic Ensemble Pruning via Greedy-Based Optimization Selection

Ensemble selection is a crucial problem for ensemble learning (EL) to speed up the predictive model, reduce the storage space requirements and to further improve prediction accuracy. Diversity among individual predictors is widely recognized as a key factor to successful ensemble selection (ES), while the ultimate goal of ES is to improve its predictive accuracy and generalization of the ensemble. Motivated by the problems stated in previous, we have devised a novel hybrid layered based greedy ensemble reduction (HLGER) architecture to delete the predictor with lowest accuracy and diversity with evaluation function according to the diversity metrics. Experimental investigations are conducted based on benchmark time series data sets, support vectors regression algorithm utilized as base learner to generate homogeneous ensemble, HLGER uses locally weight ensemble (LWE) strategies to provide a final ensemble prediction. The experimental results demonstrate that, in comparison with benchmark ensemble pruning techniques, HLGER achieves significantly superior generalization performance.
The Design of a Verified Derivative-Based Parsing Tool for Regular Expressions

We describe the formalization of Brzozowski and Antimirov derivative based algorithms for regular expression parsing, in the dependently typed language Agda. The formalization produces a proof that either an input string matches a given regular expression or that no matching exists. A tool for regular expression based search in the style of the well known GNU grep has been developed with the certified algorithms. Practical experiments conducted with this tool are reported.
Intelligent IoT Gateway

The article proposes a model of an intelligent gateway as a component for building a smart IoT (Internet of Things) network. The main advantage of this gateway is the additional ability to control end devices by using intelligent software agents. Their main purpose is to ensure the continuous and correct operation of end devices when the Internet connection is interrupted. To ensure the communication between agents and end devices, a modified protocol based on the publishing-subscribing concept provided by the MQTT (Message Queuing Telemetry Transport) broker is proposed.
Classification of ASL Alphabets and Numbers using ORB and Fast with Brief Feature Extraction and Dimensionality reduction techniques

In recent years there has been an exponential growth in the Computer Vision field, especially in the object classification domain. In this paper, various methods are compared to classify the American Sign Language (ASL) with the help of some well-known OpenCV feature descriptors and dimensionality reduction techniques. This project aims to classify signs using traditional machine learning algorithms with the best dimensionality reduction technique to get a quick response from the model and avoid complexities like seen in a CNN model. The proposed methods are compared in terms of maximum feature extraction, dimensionality reduction and accuracy of various classifiers out of which ORB with k-means has the highest accuracy of 97.08% with KNN classifier.
Applications of Artificial Intelligence (AI) in Libraries

Artificial intelligence (AI) has brought about new prospects for expanding research in all areas. The presence of artificial intelligence technologies in all spheres of work has made the future promising. The application of AI has contributed immensely to the provision and use of library information resources and has helped to achieve the goals and objectives of the library. Librarians need to be innovative in their thinking to stay relevant in their jobs because AI has found numerous applications in libraries ranging from book filing to book delivery. Its application brought about several new possibilities in the library such as connecting physical library information resources and electronic resources, and also associating video help with physical information materials and objects. The chapter discussed some components of AI, library services it can be applied to, the benefits of its application, as well as the challenges libraries face in the application of artificial intelligence in the library.