Reviewerly/exHarmony huggingface dataset:
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations

Personal narratives (PN) – spoken or written – are recollections of facts, people, events, and thoughts from one's own experience. Emotion recognition and sentiment analysis tasks are usually defined at the utterance or document level. However, in this work, we focus on Emotion Carriers (EC) defined as the segments (speech or text) that best explain the emotional state of the narrator (“loss of father”, “made me choose”). Once extracted, such EC can provide a richer representation of the user state to improve natural language understanding and dialogue modeling. In previous work, it has been shown that EC can be identified using lexical features. However, spoken narratives should provide a richer description of the context and the users' emotional state. In this paper, we leverage word-based acoustic and textual embeddings as well as early and late fusion techniques for the detection of ECs in spoken narratives. For the acoustic word-level representations, we use Residual Neural Networks (ResNet) pretrained on separate speech emotion corpora and fine-tuned to detect EC. Experiments with different fusion and system combination strategies show that late fusion leads to significant improvements for this task.
Chain centre loss: A psychology inspired loss function for image sentiment analysis

In this paper, we present a novel loss function called a chain center loss for image sentiment analysis. The proposed loss is derived from two famous deep metric loss functions, a center loss and a triplet loss, for latent space construction. Specifically, inspired by both previous loss functions, the proposed loss supervises the local and global spatial distributions. As for the local spatial distribution, the chain center loss generates center points by the Gaussian distribution and integrates visual features extracted from the images of the same sentiment. Moreover, different from the previous deep metric measures, we newly introduce sentiment correlations revealed by psychology theories into the chain center loss to capture the global spatial distribution. Concretely, the proposed loss applies anchor-related-negative triplets to the loss computation inspired by the triplet loss and updates the center points with consideration of the correlations among sentiments. As a result, with the use of the proposed loss, a latent space that strongly represents sentiment correlations can be constructed with high robustness. Improvement in the performance of the chain center loss was confirmed from experimental results.
Azure Kinect Calibration and Parameter Recommendation in Different Scenarios

The series of Kinect, which has the third-generation device now, is a low-cost scene information collection sensor to capture the color information and depth information from the scene at the same time. Due to the hardware structure of the sensor, the color image and the depth image shot by Kinect is not homologous. In this article, we compared the hardware parameters of different Kinect three generations of products, and we have calibrated the RGB camera and IR (infrared) camera of Azure Kinect, so as to the internal parameters and external parameters between the RGB camera and IR camera, and align the color image and depth image taken with Azure Kinect. At the same time, we evaluated the shooting accuracy and stability of Azure Kinect’s depth camera, and conducted experiments on the relationship between shooting error and measurement distance on three generations of products, also the parameter optimization suggestions are provided. In addition, according to different usage scenarios, we recommend the shooting parameters and the image effective range of the sensor simultaneously.
Service-Aware Personalized Item Recommendation

Current recommender systems employ item-centric properties to estimate ratings and present the results to the user. However, recent studies highlight the fact that the stages of item fruition also involve extrinsic factors, such as the interaction with the service provider before, during and after item selection. In other words, a holistic view of consumer experience, including local properties of items, as well as consumers’ perceptions of item fruition, should be adopted to enhance user awareness and decision-making. In this work, we integrate recommender systems with service models to reason about the different stages of item fruition. By exploiting the Service Journey Maps to define service-based item and user profiles, we develop a novel family of recommender systems that evaluate items by taking preference management and overall consumer experience into account. Moreover, we introduce a two-level visual model to provide users with different information about recommendation results: (i) the higher level summarizes consumer experience about items and supports the identification of promising suggestions within a possibly long list of results; (ii) the lower level enables the exploration of detailed data about the local properties of items. In a user test instantiated in the home-booking domain, we compared our models to standard recommender systems. We found that the service-based algorithms that only use item fruition experience excel in ranking and minimize the error in rating estimation. Moreover, the combination of data about item fruition experience and item properties achieves slightly lower recommendation performance; however, it enhances users’ perceptions of the awareness and the decision-making support provided by the system. These results encourage the adoption of service-based models to summarize user preferences and experience in recommender systems.
Quantum Cryptography and Quantum Key

the area of Quantum Cryptography is a new and upcoming field in terms of security of data. Unlike the normal Cryptography techniques this technique is faster and also can handle large amount of data as it works on qubits and on the principle of Heisenberg Uncertainty. Quantum cryptography deals with the security applications mainly used in Quantum Computers now a days. Furthermore, a security evaluation is given and any kind of attack that would present in any event a slip up speed of 46.875&#x0025;. The paper represents various algorithm that are used in quantum cryptography along with its comparison with the existing classical cryptography algorithms. It also includes the shortcomings of the present algorithms and the future aspect of quantum cryptography.
BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model

Binary authorship analysis is a significant problem in many software engineering applications. In this paper, we formulate a binary authorship verification task to accurately reflect the real-world working process of software forensic experts. It aims to determine whether an anonymous binary is developed by a specific programmer with a small set of support samples, and the actual developer may not belong to the known candidate set but from the wild. We propose an effective binary authorship verification framework, BinMLM. BinMLM trains the RNN language model on consecutive opcode traces extracted from the control-flow-graph (CFG) to characterize the candidate developers&#x0027; programming styles. We build a mixture-of-shared architecture with multiple shared encoders and author-specific gate layers, which can learn the developers&#x0027; combination preferences of universal programming patterns and alleviate the problem of low training resources. Through an optimization pipeline of external pre-training, joint training, and fine-tuning, our framework can eliminate additional noise and accurately distill developers&#x0027; unique styles. Extensive experiments show that BinMLM achieves promising results on Google Code Jam (GCJ) and Codeforces datasets with different numbers of programmers and supporting samples. It significantly outperforms the baselines built on the state-of-the-art feature set (4.73&#x0025; to 19.46&#x0025; improvement) and remains robust in multi-author collaboration scenarios. Furthermore, Bin-MLM can perform organization-level verification on a real-world APT malware dataset, which can provide valuable auxiliary information for exploring the group behind the APT attack.
Artificial Computed Tomography Images with Progressively Growing Generative Adversarial Network

Applications of artificial intelligence in medical imaging include classification, segmentation, and treatment planning. Using current deep-learning techniques, developing these systems requires large amounts of labelled training data. Obtaining this data is challenging due to costs, required expertise, inconsistency of imaging procedures and formatting, and patient privacy concerns. Generative adversarial networks (GANs) may alleviate some of these issues by supplying realistic artificial medical images. In this study, we trained progressively growing (PG)GAN to synthesize full-sized computed tomography (CT) images and succeeded. Performance of the PGGAN was evaluated using Fréchet Inception Distance (FID), Inception Score (IS), and Precision (P) and Recall (R). These metrics were calculated for generated, training, and validation images. The influence of dataset size was explored by varying the number of samples used to calculate each metric; this affected FID, P, and R, but not IS, which has obvious implications for comparing studies. The FID between artificial CT images from PGGAN and real validation images was 42; interestingly, FID between real training and validation images was 24. This suggests that a further reduction of 18 could be achieved by improving the generative model. Overall, artificial CT images generated by PGGAN were almost indistinguishable from real images to the human eye, although computational metrics could identify differences between them. In future work, GANs may be deployed to augment data for training medical AI systems.
fastESN: Fast Echo State Network

Echo state networks (ESNs) are reservoir computing-based recurrent neural networks widely used in pattern analysis and machine intelligence applications. In order to achieve high accuracy with large model capacity, ESNs usually contain a large-sized internal layer (reservoir), making the evaluation process too slow for some applications. In this work, we speed up the evaluation of ESN by building a reduced network called the fast ESN (fastESN) and achieve an ESN evaluation complexity independent of the original ESN size for the first time. FastESN is generated using three techniques. First, the high-dimensional state of the original ESN is approximated by a low-dimensional state through proper orthogonal decomposition (POD)-based projection. Second, the activation function evaluation number is reduced through the discrete empirical interpolation method (DEIM). Third, we show the directly generated fastESN has instability problems and provide a stabilization scheme as a solution. Through experiments on four popular benchmarks, we show that fastESN is able to accelerate the sparse storage-based ESN evaluation with a high parameter compression ratio and a fast evaluation speed.
Pose2Room: Understanding 3D Scenes from Human Activities

With wearable IMU sensors, one can estimate human poses from wearable devices without requiring visual input. In this work, we pose the question: Can we reason about object structure in real-world environments solely from human trajectory information? Crucially, we observe that human motion and interactions tend to give strong information about the objects in a scene – for instance a person sitting indicates the likely presence of a chair or sofa. To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment. P2R-Net models the probability distribution of object class as well as a deep Gaussian mixture model for object boxes, enabling sampling of multiple, diverse, likely modes of object configurations from an observed human trajectory. In our experiments we show that P2R-Net can effectively learn multi-modal distributions of likely objects for human motions, and produce a variety of plausible object structures of the environment, even without any visual information. The results demonstrate that P2R-Net consistently outperforms the baselines on the PROX dataset and the VirtualHome platform.
Brain Tumor Recognition based on Classical to Quantum Transfer Learning

We expand the idea of transfer learning, generally applied in current machine learning paradigm, to the evolving hybrid neural network contrived out of traditional and quantum components for brain tumor recognition from MRI images. The proposed model is a perfect synergy of traditional classical component and a revolutionary quantum component. The notion of traditional components is to customize a pre-trained network to extract features from brain tumor MRI images, whereas the notion of the quantum components is to employ variational quantum circuit to act as a classifier with learnable parameters. Exhaustive simulation experiments reveals the efficacy of the quantum transfer learning scheme to beat the performance of the conventional classical transfer learning.
Playing With Embodied Social Interaction: A Thematic Review of Experiments on Social Aspects in Gameful Virtual Reality

Abstract Recently, there has been a burgeoning of immersive virtual reality (VR) applications in a variety of shapes, including gameful social VR. Despite a longstanding tradition of studying social factors in gameful computing, this perspective in the specific experiences of VR is only gaining traction. This highly multidisciplinary area of interest is immensely complex with potential consequences on individuals and social groups alike. To aid in constituting and systematizing this area of research from the early days, this paper explores 14 state-of-art publications on experimental research of social aspects in gameful VR. These were analysed predominantly based on the included manipulations and the studied outcomes resulting in the extraction of five and seven thematic wholes, respectively. Finally, based on the findings, four broad avenues to consider were emphasized as suggested paths for a comprehensive future of embodied gameful social VR application and research across a variety of disciplines.
A class of twisted generalized Reed–Solomon codes

Let $${\mathbb F}_{q}$$ be a finite field of size q and $${\mathbb F}_{q}^*$$ the set of non-zero elements of $${\mathbb F}_{q}$$ . In this paper, we study a class of twisted generalized Reed–Solomon code $$\mathcal {C}_\ell (D, k, \eta , \varvec{v})\subset {\mathbb F}_{q}^n$$ generated by the following matrix $$\begin{aligned} \left( \begin{array}{cccc} v_{1} &{} v_{2} &{} \cdots &{} v_{n} \\ v_{1} \alpha _{1} &{} v_{2} \alpha _{2} &{} \cdots &{} v_{n} \alpha _{n} \\ \vdots &{} \vdots &{} \ddots &{} \vdots \\ v_{1} \alpha _{1}^{\ell -1} &{} v_{2} \alpha _{2}^{\ell -1} &{} \cdots &{} v_{n} \alpha _{n}^{\ell -1} \\ v_{1} \alpha _{1}^{\ell +1} &{} v_{2} \alpha _{2}^{\ell +1} &{} \cdots &{} v_{n} \alpha _{n}^{\ell +1} \\ \vdots &{} \vdots &{} \ddots &{} \vdots \\ v_{1} \alpha _{1}^{k-1} &{} v_{2} \alpha _{2}^{k-1} &{} \cdots &{} v_{n} \alpha _{n}^{k-1} \\ v_{1}\left( \alpha _{1}^{\ell }+\eta \alpha _{1}^{q-{2}}\right) &{} v_{2}\left( \alpha _{2}^{\ell }+ \eta \alpha _{2}^{q-2}\right) &{}\cdots &{} v_{n}\left( \alpha _{n}^{\ell }+\eta \alpha _{n}^{q-2}\right) \end{array}\right) \end{aligned}$$ where $$0\le \ell \le k-1,$$ the evaluation set $$D=\{\alpha _{1},\alpha _{2},\cdots , \alpha _{n}\}\subseteq {\mathbb F}_{q}^*$$ , scaling vector $$\varvec{v}=(v_1,v_2,\cdots ,v_n)\in ({\mathbb F}_{q}^*)^n$$ and $$\eta \in {\mathbb F}_{q}^*$$ . The minimum distance and dual code of $$\mathcal {C}_\ell (D, k, \eta , \varvec{v})$$ will be determined. For the special case $$\ell =k-1,$$ a sufficient and necessary condition for $$\mathcal {C}_{k-1}(D, k, \eta , \varvec{v})$$ to be self-dual will be given. We will also show that the code is MDS or near-MDS. Moreover, a complete classification when the code is near-MDS or MDS will be presented.
Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. Simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method.
Improving College English Reading Teaching Efficiency Based on Student Behavior Data Mining and Mobile Edge Computing

The reading scope of university English reading is constantly expanding, and the teaching content is gradually increasing. Students are unable to grasp the article’s ideas while reading, resulting in an incorrect understanding of the article and the selection of incorrect topics. In English reading, teachers should begin by broadening students’ knowledge, increasing the number of new words they learn on a regular basis, gradually building reading experience, and improving reading efficiency. Data mining (abbreviated as DM) is a method of extracting hidden, unknown, but potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical application data. Based on student behavior DM and mobile edge computing, this paper investigates strategies to improve the efficiency of university English reading instruction. Teachers and students can interact more easily with the help of university English reading teaching based on student behavior DM, and good interpersonal interaction can help students better understand and master the language. It is also beneficial for teachers to provide more tailored guidance for students’ individual university English reading teaching levels and learning abilities, as well as to assist them in developing personalized efficiency improvement strategies. The goal of DM student behavior is to discover knowledge, mine information, and apply rules without making any assumptions, with unknown, useful, and effective results.
A Comparative Evaluation of Differentially Private Image Obfuscation

Image data may contain sensitive information, such as face and iris, which can be misused if in the hands of an adversary. As image data is continuously being collected and shared, it is imperative to ensure the privacy of image data. Widely used image obfuscation methods apply blurring or pixelization to those sensitive regions. However, they are prone to inference attacks, and do not provide quantifiable privacy guarantees. Recently, several obfuscation approaches have been proposed to satisfy the rigorous notion of differential privacy. The goal of this work is to provide a comparative evaluation of those previously proposed approaches in the context of obfuscating face and iris images. We synthesize existing differentially private obfuscation methods and analyze their privacy guarantees. Furthermore, we conduct an extensive empirical evaluation regarding practical utility and privacy protection, with real-world face and iris image datasets. We find that DP-SVD outperforms other methods on several privacy and utility measures. Moreover, we provide an in-depth discussion of our results and point to several considerations when applying those differentially private image obfuscation methods.
In Pursuit of Babel - Multilingual End-to-End Spoken Language Understanding

End-to-end spoken language understanding (E2E SLU) systems predict the utterance semantics directly from speech. So far, to the best of our knowledge, E2E models have only been trained to recognize the semantics for a single language. In this work we introduce the first multilingual E2E SLU system and present results across three languages - English, Spanish and French. We propose a transformer-based, multilingual acoustic encoder to predict intents, that leverages pre-training for both acoustic and linguistic modalities of the SLU model. It learns a robust, cross-modal latent space using a pre-trained multilingual BERT as a semantic teacher. The best performing model achieves relative improvements of 7.2% in a single language setting, 5-6% in two, and 4-6% in three language settings. An intent-wise analysis shows that semantic supervision becomes more important for shorter utterances, while providing an explicit language identifier at the input leads to lower intent classification errors.
View Enhanced Jigsaw Puzzle for Self-Supervised Feature Learning in 3D Human Action Recognition

Self-supervised learning methods have received much attention in skeleton-based human action recognition.These methods rely on pretext tasks to utilize unlabeled data and learn an effective feature encoder.In this paper, a novel self-supervised learning method is proposed.First, we design a new pretext task called view enhanced jigsaw puzzle (VEJP) to improve the learning difficulty of the encoder.The VEJP introduces multi-view information into the jigsaw puzzle, thus forcing the encoder to learn view-independent high-level features of human skeletons.Based on the encoder trained by VEJP, we propose the view pooling encoder (VPE) to integrate the information of multiple views with the pooling mechanism, and the features extracted by VPE are more robust and distinguishable.In addition, by adjusting the difficulty of VEJP, the influence of the pretext task difficulty on the downstream task performance is studied, and the experimental results show that the pretext tasks should be moderately difficult to achieve effective feature learning.Our method achieves competitive results on representative benchmark datasets.It provides a strong baseline for the jigsaw puzzle task and shows advantages in situations where the number of labeled data is minimal.
Twitter Sentiment Analysis using Natural Language Processing

Social media is the richest source of text generated by the user. So there is a necessity to automate the system to help organizing and classifying the opinions posted on social media sites. Proposed methodology framework using Artificial Recurrent Neural Network (ARNN) with bi-directional long short term memory (LSTM) has been used for the classification of sentiments. Structure for RNN with bidirectional LSTM is depicted. US airline Twitter sentiment dataset has been analysed using bidirectional LSTM model. Text with varying length is taken for the experiment. Graphical representation of the analysis has been depicted in this paper. Confusion matrix shows the result. At the end it is concluded that the sentiments are analysed and classified as positive, negative or neutral.