Reviewerly/exHarmony huggingface dataset:
Universal quantum circuit evaluation on encrypted data using probabilistic quantum homomorphic encryption scheme*

Homomorphic encryption has giant advantages in the protection of privacy information. In this paper, we present a new kind of probabilistic quantum homomorphic encryption scheme for the universal quantum circuit evaluation. Firstly, the pre-shared non-maximally entangled states are utilized as auxiliary resources, which lower the requirements of the quantum channel, to correct the errors in non-Clifford gate evaluation. By using the set synthesized by Clifford gates and T gates, it is feasible to perform the arbitrary quantum computation on the encrypted data. Secondly, our scheme is different from the previous scheme described by the quantum homomorphic encryption algorithm. From the perspective of application, a two-party probabilistic quantum homomorphic encryption scheme is proposed. It is clear what the computation and operation that the client and the server need to perform respectively, as well as the permission to access the data. Finally, the security of probabilistic quantum homomorphic encryption scheme is analyzed in detail. It demonstrates that the scheme has favorable security in three aspects, including privacy data, evaluated data and encryption and decryption keys.
Lattice-Free Mmi Adaptation of Self-Supervised Pretrained Acoustic Models

In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model. We pretrain a Transformer model on thousand hours of untranscribed Librispeech data followed by supervised adaptation with LFMMI on three different datasets. Our results show that fine-tuning with LFMMI, we consistently obtain relative WER improvements of 10% and 35.3% on the clean and other test sets of Librispeech (100h), 10.8% on Switchboard (300h), and 4.3% on Swahili (38h) and 4.4% on Tagalog (84h) compared to the baseline trained only with supervised data.
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values

Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few. In lockstep, regularization methods, which aim to prevent overfitting by penalizing the weight connections, or turning off some units, have been widely studied either. In this paper, we present a novel approach called MaxDropout, a regularizer for deep neural network models that works in a supervised fashion by removing (shutting off) the prominent neurons (i.e., most active) in each hidden layer. The model forces fewer activated units to learn more representative information, thus providing sparsity. Regarding the experiments, we show that it is possible to improve existing neural networks and provide better results in neural networks when Dropout is replaced by MaxDropout. The proposed method was evaluated in image classification, achieving comparable results to existing regularizers, such as Cutout and RandomErasing, also improving the accuracy of neural networks that uses Dropout by replacing the existing layer by MaxDropout.
High‐Temperature VIS‐IR Spectroscopy of NH<sub>4</sub>‐Phyllosilicates

Abstract Ammonium phyllosilicates have been identified on the dwarf planet Ceres, thanks to infrared telescopic and orbital data from the Dawn mission, by means of the 3.06 μm spectral feature. Nevertheless, it is not known which ammonium‐bearing phyllosilicate species are present, nor the thermal processing they underwent throughout Ceres history. Identifying the NH 4 + ‐hosting mineral species is important for deciphering Ceres’ surface mineralogy, which provides a link to its interior and putative different evolutionary pathways. Ammoniated species can have formed in the presence of water/ammonia‐rich fluids in different conditions in the interior of the planet; in case of an exogenous outer Solar System origin, they can have undergone heating at depth. In this work, we study the visible‐infrared spectra of several NH 4 ‐treated/untreated phyllosilicates in the range 0.35–5 μm, acquired in vacuum and at temperatures between 298 and 723 K. Previously NH 4 ‐phyllosilicates have been mostly studied at ambient condition, preventing the characterization of the NH 4 + band at 3.06 μm, due to overlapping bands of water. With this new set of measurements, we investigate how the NH 4 ‐phyllosilicates spectra are modified when the mineral’s water is lost, and which temperature is the limit for the releasing of NH 4 + . We present the first high‐temperatures/high vacuum 3‐μm reflectance spectra of ammonium phyllosilicates. Our measurements indicate that Mg‐phyllosilicates are the best candidates for the ammonium‐bearing species. Moreover, the almost complete disappearing of NH 4 + absorption feature at ∼3.06 μm for ammoniated phyllosilicates heated at the highest temperatures indicates that such species on Ceres could not have experienced temperatures higher than 623 K.
Change analysis in land use land cover due to surface mining in Jharia coalfield through Landsat time series data

Mining and reclamation are the main drivers for land use land cover changes (LULC) in Jharia coalfield. In this study, impact of mining on surrounding area’s ecology was analysed by calculating the percentage changes of accurately classified images of different time period. The landsat imagery from 1999, 2009 and 2015 were used to map land cover changes for Jharia coalfield. The landsat data used, were L1T standard data product which was radiometrically calibrated and orthorectified. An unsupervised ISODATA clustering method along with matrix union function was used, to map the land cover of mining areas and to study the changes in LULC for Jharia coalfield. The results reveal that between the ten-year interval from 1999 to 2009, the percentage increase in mining was 81.72% and in mean time about 8.46% area had undergone changes from mines to vegetative reclaimed mines, whereas in the five-year interval from 2009 to 2015 the percentage increase in mining was 14.20% and about 8.92% area of mining was changed to vegetative reclaimed mines. This study also reveals that five-year interval image characterizes the classes in a better way and provides much clearer view in changes than ten-year interval image. The overall classification accuracy of all the satellite imagery is above 90%, which indicate that ISODATA classification techniques provide better results of classification. This study of change analysis will help to quantify the changes and will provide the supporting platform for decision making in environmental management plan for mining.
GazeBar: Exploiting the Midas Touch in Gaze Interaction

Imagine an application that requires constant configuration changes, such as modifying the brush type in a drawing application. Typically, options are hierarchically organized in menu bars that the user must navigate, sometimes through several levels, to select the desired mode. An alternative to reduce hand motion is the use of multimodal techniques such as gaze-touch, that combines gaze pointing with mechanical selection. In this paper, we introduce GazeBar, a novel multimodal gaze interaction technique that uses gaze paths as a combined pointing and selection mechanism. The idea behind GazeBar is to maximize the interaction flow by reducing ”safety” mechanisms (such as clicking) under certain circumstances. We present GazeBar’s design and demonstrate it using a digital drawing application prototype. Advantages and disadvantages of GazeBar are discussed based on a user performance model.
Yolo+FPN: 2D and 3D Fused Object Detection With an RGB-D Camera

In this paper we propose a new deep neural network system, called Yolo+FPN, which fuses both 2D and 3D object detection algorithms to achieve better real-time object detection results and faster inference speed, to be used on real robots. Finding an optimized fusion strategy to efficiently combine 3D object detection with 2D detection information is useful and challenging for both indoor and outdoor robots. In order to satisfy real-time requirements, a trade-off between accuracy and efficiency is needed. We not only have improved training and test accuracies and lower mean losses on the KITTI object detection benchmark comparing with our baseline method, but also achieve competitive average precision on 3D detection of all classes in three levels of difficulty comparing with other state-of-the-art methods. Also, we implemented Yolo+FPN system using an RGB-D camera, and compared the speed of object detection using different GPUs. For the real implementation of both indoor and outdoor scenes, we focus on person detection, which is the most challenging and important among the three classes.
Enhanced Low-cost Web-based Virtual Tour Experience for Prospective Students

This paper presents a web-based Virtual Tour (VT) pipeline to allow organisations to develop a VT experience rapidly, which integrates a collaborative virtual environment with avatars and a 3D Environment to provide visitors an interactive and shareable experience.Users from an organisation with minimal coding experience can develop a low-cost web-based VT using a 360 camera, the Open Source tools Marzipano and Hubs, the developed software framework and documentation directly from the GitHub repository.The contribution of this work is both the software framework for use with the Marzipano, Mozilla Hubs tool and Tin Can API.A University case study along with a user evaluation demonstrate the effectiveness of the approach.
Prelarge-Based Utility-Oriented Data Analytics for Transaction Modifications in Internet of Things

The Internet of Things (IoT) environment includes things that exchange information using data generated through sensors. The IoT technology can be executed efficiently by providing the necessary information to things instead of transmitting all the data. There is a need for an algorithm that can extract meaningful information for the things from the data. High utility pattern mining, which can handle the characteristics of real-world databases better than traditional pattern mining methods, has been actively researched. The traditional high utility pattern mining techniques find meaningful patterns from static databases. Therefore, these techniques are not suitable for the dynamically changing databases in the real world. In order to solve this problem, a variety of methods that consider the modifications, deletions, and insertions of transactions are proposed. The prelarge concept, which can be efficiently operated by reducing the rescanning of the original database using two thresholds, was also proposed in order to handle the limitations of the methods that are used for static databases. In the prelarge concept, the large patterns and the prelarge patterns that are discovered are maintained and used for the next transaction change. In this article, we proposed a new method pre-large based incremental high utility pattern mining for transaction modification (PIHUP-MOD) implemented as a tree structure in order to handle the transaction modifications. The method has an efficient structure and techniques to mine high utility patterns in the transaction modifications with the prelarge concept. We conducted the performance evaluation, and the experimental results on the real datasets and the synthetic datasets showed that the proposed approach has a better performance than the state-of-the-art approaches in terms of the runtime, memory usage, and scalability.
Injecting domain knowledge in multi-objective optimization problems: A semantic approach

In the field of complex problem optimization with metaheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker’s preferences, or algorithms. However, there is a lack of approaches where ontologies are applied in a direct way into the optimization process, with the aim of enhancing it by allowing the systematic incorporation of additional domain knowledge. This is due to the high level of abstraction of ontologies, which makes them difficult to be mapped into the code implementing the problems and/or the specific operators of metaheuristics. In this paper, we present a strategy to inject domain knowledge (by reusing existing ontologies or creating a new one) into a problem implementation that will be optimized using a metaheuristic. Thus, this approach based on accepted ontologies enables building and exploiting complex computing systems in optimization problems. We describe a methodology to automatically induce user choices (taken from the ontology) into the problem implementations provided by the jMetal optimization framework. With the aim of illustrating our proposal, we focus on the urban domain. Concretely, we start from defining an ontology representing the domain semantics for a city (e.g., building, bridges, point of interest, routes, etc.) that allows defining a-priori preferences by a decision maker in a standard, reusable, and formal (logic-based) way. We validate our proposal with several instances of two use cases, consisting in bi-objective formulations of the Traveling Salesman Problem (TSP) and the Radio Network Design problem (RND), both in the context of an urban scenario. The results of the experiments conducted show how the semantic specification of domain constraints are effectively mapped into feasible solutions of the tackled TSP and RND scenarios. This proposal aims at representing a step forward towards the automatic modeling and adaptation of optimization problems guided by semantics, where the annotation of a human expert can be now considered during the optimization process.
A lightweight object detection algorithm based on YOLOv3 for vehicle and pedestrian detection

YOLOv3, the third version of the YOLO family, performs significantly well on object detection. Nevertheless, using YOLOv3 for real-time vehicle and pedestrian detection on unmanned vehicles with limited computing resources is still a very big challenge due to the high computational complexity of YOLOv3. In this paper, a new network architecture for vehicle and pedestrian detection based on YOLOv3 is proposed which is named as Lightweight-YOLOv3. Three improvements are presented in Lightweight-YOLOv3. Firstly, to reduce the model size and computing complexity, channel and layer pruning is proposed by introducing L1 regularization on the batch normalization layer. Thus, unimportant channels and layers are recognized and removed. Secondly, to reduce the missed detection in crowded scenes and locate targets better, the MergeSoft-NMS which merges the bounding boxes with high overlap is designed based on Soft-NMS. Thirdly, considering the obvious aspect ratio of vehicle and pedestrian, the anchor boxes which are designed based on multi-class is redesigned for better vehicle and pedestrian matching and localization in Lightweight-YOLOv3. In the experiment, compared with YOLOv3 and YOLOv3-tiny, Lightweight-YOLOv3 which performs well on detection accuracy and speed is effective and compact for vehicle and pedestrian detection.
Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.
Reference Revamp: A 1-2 Model for Training and Tiered Services

The UCLA Science Libraries improved upon our single service points by creating a team-based, tiered research assistance model to foster student employee skill development. This model was further developed to expand training for public services staff, enabling librarians and student research assistants to move beyond desk-based services. This multi-phased approach involved restructured training and the development of collaborative, tiered services. Librarians utilized train-the-trainer sessions, detailed documentation toolkits, and a robust outreach plan to ensure success. After initial implementation of this new model, librarians observed above average use of the service and excellent user feedback. Its versatility has also played a direct role in the successful transition from physical to virtual services in light of the COVID-19 pandemic.
True Random Number Generation using Latency Variations of Commercial MRAM Chips

The emerging magneto-resistive RAM (MRAM) has considerable potential to become a universal memory technology because of its several advantages: unlimited endurance, lower read/write latency, ultralow-power operation, high-density, and CMOS compatibility, etc. This paper will demonstrate an effective technique to generate random numbers from energy-efficient consumer-off-the-shelf (COTS) MRAM chips. In the proposed scheme, the inherent (intrinsic/extrinsic process variation) stochastic switching behavior of magnetic tunnel junctions (MTJs) is exploited by manipulating the write latency of COTS MRAM chips. This is the first system-level experimental implementation of true random number generator (TRNG) using COTS toggle MRAM technology to the best of our knowledge. The experimental results and subsequent NIST SP-800-22 suite test reveal that the proposed latency-based TRNG is acceptably fast (~ 22Mbit/s in the worst case) and robust over a wide range of operating conditions.
Research on the Application of Collaborative Filtering Algorithm in Mobile E-Commerce Recommendation System

With the rise of e-commerce in the Internet industry, e-commerce recommendation system has been widely concerned. Electrical contractor recommendation system is based on the user's personal information such as features, historical behavior and items through collaborative filtering, content filtering, knowledge discovery, interactive and recommend recommendation technology, referring users to the items that may be of interest, in many recommendation algorithm, collaborative filtering recommendation technology is currently one of the most widely used recommendation algorithm, this paper test the collaborative filtering algorithm in the application of mobile electronic commerce recommendation system experiment, it is concluded that the realization of the collaborative filtering algorithm is better personalized recommendation.
Attention uncovers task-relevant semantics in emotional narrative understanding

Attention mechanisms in deep neural network models have helped them to achieve exceptional performance at complex natural language processing tasks. Previous attempts to investigate what these models have been “paying attention to” suggest that these attention representations capture syntactic information, but there is less evidence for semantics. In this paper, we investigate the capability of an attention mechanism to “attend to” semantically meaningful words. Using a dataset of naturalistic emotional narratives, we first build a Window-Based Attention (WBA) consisting of a hierarchical, two-level long short-term memory (LSTM) with softmax attention. Our model outperforms state-of-the-art models at predicting emotional valence, and even surpassing average human performance. Next, we show in detailed analyses, including word deletion experiments and visualizations, that words that receive higher attention weights in our model also tend to have greater emotional semantic meaning. Experimental results using six different pre-trained word embeddings suggest that deep neural network models which achieve human-level performance may learn to place greater attention weights on words that humans find semantically meaningful to the task at hand.
DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT volume

Automatic liver tumor segmentation from 3D Computed Tomography (CT) is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) in liver tumor segmentation draw on success of learning discriminative multi-level features. In this paper, we propose a Deep Attentive Refinement Network (DARN) for improved liver tumor segmentation from CT volumes by fully exploiting both low and high level features embedded in different layers of FCN. Different from existing works, we exploit attention mechanism to leverage the relation of different levels of features encoded in different layers of FCN. Specifically, we introduce a Semantic Attention Refinement (SemRef) module to selectively emphasize global semantic information in low level features with the guidance of high level ones, and a Spatial Attention Refinement (SpaRef) module to adaptively enhance spatial details in high level features with the guidance of low level ones. We evaluate our network on the public MICCAI 2017 Liver Tumor Segmentation Challenge dataset (LiTS dataset) and it achieves state-of-the-art performance. The proposed refinement modules are an effective strategy to exploit multi-level features and has great potential to generalize to other medical image segmentation tasks.