Reviewerly/exHarmony huggingface dataset:
CallChain: Identity Authentication Based on Blockchain for Telephony Networks

Telephony networks serve as a reliable communication channel, and phone numbers are usually used to authenticate users' identities in telephony networks. However, modern telephony networks does not provide end-to-end authentication for phone numbers, making it difficult for a receiver to confirm a caller's real identity. Moreover, due to the lack of effective authentication solutions for users, phone numbers are usually exploited to commit identity theft, credit card fraud, or telecom harassment by phone number spoofing attacks. In this paper, we propose a blockchain-based identity authentication system for telephony networks, CallChain. CallChain issues verifiable digital identities to users which bound a user to his/her phone number. Based on the digital identity, CallChain provides end-to-end identity authentication between the caller and receiver in the data channel without modifying the core technologies in telephony networks. In the proposed system, users first register decentralized identities (DIDs) and store them in the blockchain distributed ledger. Then Call Center issues verifiable PhoneNumber Credentials to users. And finally users conduct end-to-end identity authentication based on these PhoneNumber Credentials before the call is established. We implement a prototype as an App for Android-based phones based on an open source blockchain platform Hyperledger Indy. Moreover, we detect that CallChain only adds a worst-case 2.1 seconds call establishment overhead and can resist DDoS attacks effectively.
A compact low-power gaze gesture sensor based on laser feedback interferometry for smart glasses

The integration of gaze gesture sensors in next-generation smart glasses will improve usability and enable new interaction concepts. However, consumer smart glasses place additional requirements to gaze gesture sensors, such as a low power consumption, high integration capability and robustness to ambient illumination. We propose a novel gaze gesture sensor based on laser feedback interferometry (LFI), which is capable to measure the rotational velocity of the eye as well as the sensors distance towards the eye. This sensor delivers a unique and novel set of features with an outstanding sample rate allowing to not only predict a gaze gesture but also to anticipate it. To take full advantage of the unique sensor features and the high sampling rate, we propose additionally a novel gaze gesture classification algorithm based on single sample. At a mean F1-score of 93.44 performance at a negative latency between gaze gesture input and command execution.
English Teaching Effect Evaluation Based on Data Association Mining

Cognitive approach holds that students are the center of teaching, and teaching should focus on students' activities. English teaching should be based on students' practice. Students should be allowed to master the ability of using foreign languages by participating in active and large amount of language activities. According to constructivism learning theory, cognitive theory and humanistic learning theory, this paper puts forward a collaborative learning environment model based on computer multimedia network environment, and details the specific description of the implementation mode. In this paper, the data association mining algorithm is used to process and analyze the big data of learning effect. The experimental results show that the method is more accurate and more targeted than the traditional methods in English teaching evaluation. This method has a certain reference value for the evaluation of English teaching effect.
Learning Alternating Deep-Layer Cascaded Representation

We propose an alternating deep-layer cascade (A-DLC) architecture for representation learning in the context of image classification. The merits of the proposed model are threefold. First, A-DLC is the first-ever method that alternatively cascades the sparse and collaborative representations using the class-discriminant softmax vector representation at the interface of each cascade section so that the sparsity and collaborativity can simultaneously be considered. Second, A-DLC inherits the hierarchy learning capability that effectively extends the traditional shallow sparse coding to a multi-layer learning model, thus enabling a full exploitation of the inherent latent discriminative information. Third, the simulation results show a significant amelioration in the classification accuracy, compared to earlier one-step single-layer classification algorithms. The Matlab code of this paper is available at https://github.com/chenzhe207/A-DLC.
Classification of Human Activities using data captured through a smartphone using deep learning techniques

Classification and recognition of activities performed by humans is an essential task when we are into an era of connected-sensors and Industry 4.0, also referred to as the Internet of Things (IoT). In this paper, we propose an intelligent human activity-recognition system that can automatically predict daily human activities based on data acquired by sensors, using deep learning techniques like Two-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The data collected using the sensors is fed to CNN and LSTM network. The proposed model is trained and tested on the WISDM dataset. The proposed deep learning architecture has Batch Normalization (BN) and dropout layers to avoid overfitting. The proposed architecture is of low complexity and achieves high accuracy and F1 score. The final accuracy obtained using the LSTM model is up to 97.5% with 21,922 trainable parameters. The obtained results are represented with the help of a convolutional matrix and learning curves. The results are quite promising and can be utilized without any restrictions on environments or domain-structures.
IK-prototypes: Incremental Mixed Attribute Learning Based on K-Prototypes Algorithm, a New Method

Humongous amount of data is continuously generated by thousands of data sources, which simultaneously send data records that include a wide variety of elements of mixed types such as electronic purchases, information from social networks. They should be processed sequentially and incrementally over flexible time windows and then used for different analyzes. On account on these new instances that includes new attributes which have to be learned as development proceeds, called data stream, this paper tackles the incremental attribute learning task for mixed data using k-prototypes algorithm. Firstly, we propose a novel Incremental k-prototypes algorithm based on the merge technique to ensure the attribute learning task. Subsequently, we present the experiments to evaluate our new method using several real mixed data sets. The results show an improvement of the performance of the k-prototypes algorithm used for incremental attribute learning where the proposed Incremental k-prototypes method gives better results regarding the k-prototypes algorithms.
Noise-insensitive discriminative subspace fuzzy clustering

Discriminative subspace clustering (DSC) can make full use of linear discriminant analysis (LDA) to reduce the dimension of data and achieve effective clustering high-dimension data by clustering low-dimension data in discriminant subspace. However, most existing DSC algorithms do not consider the noise and outliers that may be contained in data sets, and when they are applied to the data sets with noise or outliers, and they often obtain poor performance due to the influence of noise and outliers. In this paper, we address the problem of the sensitivity of DSC to noise and outlier. Replacing the Euclidean distance in the objective function of LDA by an exponential non-Euclidean distance, we first develop a noise-insensitive LDA (NILDA) algorithm. Then, combining the proposed NILDA and a noise-insensitive fuzzy clustering algorithm: AFKM, we propose a noise-insensitive discriminative subspace fuzzy clustering (NIDSFC) algorithm. Experiments on some benchmark data sets show the effectiveness of the proposed NIDSFC algorithm.
ProYOLOv4: Some Improvements for YOLOv4

There are many functions that can improve the accuracy of the target detection model. Usually, it is necessary to test the combination of these functions on a large number of data sets and prove the results theoretically. This paper starts from the data enhanceme-nt algorithm and carries on the comparison and the theory analysis to it. A new data enhancement algorithm is obtained. And the function replacement test is carried out on the current YOLOV4 target detection model with good performance. After using the new data enhanceme-nt algorithm to replace Mosaic algorithm of Yolov4, total loss decreased by 4.69% and Val loss decreased by 9.26%. Finally, after comparing the recognition effect with the YOLOV4 model, it is found that the improved YOLOV4 model has higher confidence for the identified object.
Confirmatory Factor Analysis

Factor analysis, first developed over a century ago (Spearman 1904, 1927), attempts to explain the covariation and variance of a set of observed variables using a more parsimonious number of latent variables. Many observed variables in the social sciences, such as responses to items, are correlated. The correlation that these variables share can be explained using a theoretical latent variable, sometimes referred to as a factor. For example, suppose you develop a test comprised of constructed-response items purported to measure algebra skills. After you administer the test to a large sample of eighth graders, you notice that many of the items are correlated, indicating that students who have better algebra skills tended to score higher on each item compared to those who have lesser algebra skills. This correlation among the items is an indication that the items may be associated with a common latent variable represented by math proficiency in algebra. In other words, the observed variables, referred to as indicators, are correlated because they are influenced by a common factor, which is unobservable (such as math proficiency, anxiety). Factor analysis was developed to capture this common relationship shared among a set of observed variables.
Implicit Error, Uncertainty and Confidence in Visualization: An Archaeological Case Study

While we know that the visualization of quantifiable uncertainty impacts the confidence in insights, little is known about whether the same is true for uncertainty that originates from aspects so inherent to the data that they can only be accounted for qualitatively. Being embedded within an archaeological project, we realized how assessing such qualitative uncertainty is crucial in gaining a holistic and accurate understanding of regional spatio-temporal patterns of human settlements over millennia. We therefore investigated the impact of visualizing qualitative implicit errors on the sense-making process via a probe that deliberately represented three distinct implicit errors, i.e., differing collection methods, subjectivity of data interpretations and assumptions on temporal continuity. By analyzing the interactions of 14 archaeologists with different levels of domain expertise, we discovered that novices became more actively aware of typically overlooked data issues and domain experts became more confident of the visualization itself. We observed how participants quoted social factors to alleviate some uncertainty, while in order to minimize it they requested additional contextual breadth or depth of the data. While our visualization did not alleviate all uncertainty, we recognized how it sparked reflective meta-insights regarding methodological directions of the data. We believe our findings inform future visualizations on how to handle the complexity of implicit errors for a range of user typologies and for highly data-critical application domains such as the digital humanities.
An Internet Of Things (IoT) Forensics Model Using Third-Party Logs-Vault

Since the last decade, the number of Internet of Things (IoT) devices has increased dramatically. Consequently, the number of effected users and persons has also grown. By 2025 the number of connected IoT devices will reach 75.44 billion. This tremendous increase in IoT devices environments has increased the IoT threats, risks, and digital crimes. Thus, an efficient IoT forensic model should be laid out to fulfill the investigation requirements in IoT environments. In this paper, a new IoT forensics model has been introduced to face the IoT forensics challenges. The proposed model has satisfied the security, confidentiality, integrity, availability, authentication, and non-repudiation requirements.
GOAT at the FinSim-2 task: Learning Word Representations of Financial Data with Customized Corpus

In this paper, we present our approaches for the FinSim 2021 Shared Task on Learning Semantic Similarities for the Financial Domain. The aim of the FinSim shared task is to automatically classify a given list of terms from the financial domain into the most relevant hypernym (or top-level) concept in an external ontology. Two different word representations have been compared in our study, i.e., customized word2vec provided by the shared task and FinBERT. We first create a customized corpus from the given prospectuses and relevant articles from Investopedia. Then we train the domain-specific word2vec embeddings using the customized data with customized word2vec and FinBERT as the initialized embeddings respectively. Our experimental results demonstrate that these customized word embeddings can effectively improve the classification performance and achieve better results than the direct utilization of the provided word embeddings. The class imbalance issue of the given data is also explored. We empirically study the classification performance by employing several different strategies for imbalanced classification problems. Our system ranks 2nd on both Average Accuracy and Mean Rank metrics.
SRPS–deep-learning-based photometric stereo using superresolution images

Abstract This paper introduces a novel deep-learning-based photometric stereo method that uses superresolution (SR) images: SR photometric stereo. Recent deep-learning-based SR algorithms have yielded great results in terms of enlarging images without mosaic effects. Supposing that the SR algorithms successfully enhance the feature and colour information of original images, implementing SR images using the photometric stereo method facilitates the use of considerably more information on the object than existing photometric stereo methods. We built a novel deep-learning-based network for the photometric stereo technique to optimize the input–output of SR image inputs and normal map outputs. We tested our network using the most widely used benchmark dataset and obtained better results than existing photometric stereo methods.
The Four C's, Constructivism, and Digitizing Curriculum

It is estimated that by 2055 humans will have entered the fourth industrial revolution, a period where technology devices will replace or redefine the human workforce. In preparation, countries around the world have transitioned their educational practices to address the needs of the ever-changing global economy and technology advancements. This shift towards preparing a technology literate workforce is frequently referred to as 21st century skills, the implementation of curriculum which meets the projected needs of learners in the future workforce. Although educators are encouraged to integrate technology in training to better equip the learner in navigating the continuously evolving digital workspace, how this is done is less clear. The purpose of this chapter is to share an inclusive method of how educational systems can digitize constructivism with 21st century skills to ensure all students are prepared for the global workforce.
Population transfer in a superconducting qutrit via shortcut to adiabaticity with optimized drivings

An efficient scheme is proposed for implementing fast population transfer in a superconducting qutrit via shortcut to adiabaticity (STA) with optimized drivings. An artificial atom of Cooper-pair box, having sufficient level anharmonicity, resonantly interacts with two classical microwave drivings. At two-photon resonance, the dynamical behavior of the qutrit can be described by a two-state system. By the technique of STA of counterdiabatic driving, the non-adiabatic coupling between two eigenstates can be nullified. Without increasing the number of applied drivings, we can implement the target state transfer only by modifying two original Gaussian-type pulses. Compared with the pulses required by the invariant-based operation, the modified pulses have the time profiles that are more accessible in experiment. With the available decoherence rates, high-fidelity population transfer can be obtained numerically. The strategy could offer a promising avenue towards optimized control over superconducting qutrits experimentally.
Black Hole—White Hole Algorithm for Dynamic Optimization of Chemically Reacting Systems

Black hole (BH) meta-heuristic optimization is a nature-inspired algorithm which mimics the behavior of black holes. The Black hole algorithm framework is implemented in this work to solve dynamic optimization of some benchmark problems encountered in chemically reacting systems. We have employed two different algorithm types which include a standalone black hole algorithm and an algorithm which combines exploration–exploitation tradeoff in the algorithm. This tradeoff is enabled through the concept of white hole (WH). We have employed both piecewise linear and piecewise constant control profiles in our simulations. The algorithms proposed are simple and are easy to implement. Case studies studied show that the novel algorithms are robust and compare very well with existing algorithms.
RecipeGM: A Hierarchical Recipe Generation Model

This paper demonstrates the application of hierarchical convolutional neural networks using self-attention mechanisms for the task of generating recipes given a set of ingredients the recipe should contain. We compare this model, RECIPEGM, to an LSTM baseline and RecipeGPT using several metrics and show that our model is able to outperform even RecipeGPT in some cases. Furthermore, this work discusses suitable evaluation techniques for recipe generation and highlights weak points of some current in use metrics.
A new multi-prototype based clustering algorithm

K-means is a well-known prototype based clustering algorithm for its simplicity and efficiency. However, most k-means methods assume different classes are represented by one prototype, which makes a limit of k-means algorithms. Recently, multi-prototype clustering methods have been raised to tackle this problem, which composed of two stages: split stage and merge stage. For multi-prototype algorithms, a proper prototype number plays a vital role in the algorithm performance and it is generally given by users in a trial and error way. In this paper, a new incremental k-means clustering algorithm is designed to determine the appropriate prototype number automatically. Firstly, a new indicator is presented to judge whether the number of prototype is appropriate in the split stage. Secondly, a new merge indicator is defined according to the distance formula from datapoint to hyperplane in the merge stage. Finally, simulation results on 8 datasets illustrate the effectiveness and superiority of the proposed algorithm.
Model Compression via Collaborative Data-Free Knowledge Distillation for Edge Intelligence

Model compression without the original data for fine-tuning is challenging for deploying large-size models on resource constrained edge devices. To this end, we propose a novel data-free model compression framework based on knowledge distillation (KD), where multiple teachers are utilized in a collaborative manner to enable reliable distillation. It mainly consists of three components: adversarial data generation, multi-teacher KD, and adaptive outputs aggregation. In particular, some synthesized data are generated in an adversarial manner to mimic the original data for model compression. Then a multi-header module is developed to simultaneously leverage diverse knowledge from multiple teachers. The distillation outputs are adaptively aggregated for final prediction. The experimental results demonstrate that our framework outperforms the data-free counterpart significantly (4.48% on MNIST and 2.96% on CIFAR-10). Effectiveness of different components of our method is also verified via carefully designed ablation study.
Account Guarantee Scheme

In blockchain networks, reaching effective supervision while maintaining anonymity to the public has been an ongoing challenge. In existing solutions, certification authorities need to record all pairs of identities and pseudonyms, which is demanding and costly. This article proposed an account guarantee scheme to realize feasible supervision for existing anonymous blockchain networks with lower storage costs. Users are able to guarantee anonymous accounts with account guarantee key pairs generated from certificated polynomial functions, which inherently maintains one-to-n mapping certifications. Single or limited account guarantee key pairs do not leak privacy. Victims are able to request TCs to screen a cheater or disclose a cheater with enough fraud transactions by themselves. Detailed security and privacy analysis showed that the account guarantee scheme preserves user privacy and realizes feasible supervision. Experimental results demonstrated that the account guarantee scheme is efficient and practical.
Hardware Efficient Modified CNN Architecture for Traffic Sign Detection and Recognition

Traffic sign recognition is a vital part for any driver assistance system which can help in making complex driving decision based on the detected traffic signs. Traffic sign detection (TSD) is essential in adverse weather conditions or when the vehicle is being driven on the hilly roads. Traffic sign recognition is a complex computer vision problem as generally the signs occupy a very small portion of the entire image. A lot of research is going on to solve this issue accurately but still it has not been solved till the satisfactory performance. The goal of this paper is to propose a deep learning architecture which can be deployed on embedded platforms for driver assistant system with limited memory and computing resources without sacrificing on detection accuracy. The architecture uses various architectural modification to the well-known Convolutional Neural Network (CNN) architecture for object detection. It uses a trainable Color Transformer Network (CTN) with the existing CNN architecture for making the system invariant to illumination and light changes. The architecture uses feature fusion module for detecting small traffic signs accurately. In the proposed work, receptive field calculation is used for choosing the number of convolutional layer for prediction and the right scales for default bounding boxes. The architecture is deployed on Jetson Nano GPU Embedded development board for performance evaluation at the edge and it has been tested on well-known German Traffic Sign Detection Benchmark (GTSDB) and Tsinghua-Tencent 100k dataset. The architecture only requires 11 MB for storage which is almost ten times better than the previous architectures. The architecture has one sixth parameters than the best performing architecture and 50 times less floating point operations per second (FLOPs). The architecture achieves running time of 220[Formula: see text]ms on desktop GPU and 578 ms on Jetson Nano which is also better compared to other similar implementation. It also achieves comparable accuracy in terms of mean average precision (mAP) for both the datasets.