Reviewerly/exHarmony huggingface dataset:
Remote Supervision in Times of Pandemic at the University Level Under the Systemic Approach

In this research work, the remote supervision of teachers is shown to be carried out within the framework of Covid-19 at the University of Sciences and Humani-ties, where full and part-time teachers and also students had training in the use of digital tools for the development of their digital skills and competencies. Howev-er, teachers and students had limitations in connectivity and the minimum condi-tions such as the use of PCs or smartphones for the realization of the classes. The soft systems and the Wilson methodologies were used with a systemic approach, based on its Maltese cross where information was obtained on the perception of the teaching-learning process in remote time by the student, teacher, assistant, co-ordinator of the area, teaching assistant who were directly involved in the super-vision of remote classes. The objective of the article is to make a proposal for continuous improvement in remote learning teaching in times of pandemic. The result obtained from the research was that remote supervision allowed the teach-ing-learning process to be improved by providing feedback on continuous im-provement. It is concluded that the supervision of teachers requires teachers of full time, as well as more training in the use of digital tools, benefiting students from research.
Psychological Aspects of the Organization’s Information Security in the Context of Socio-engineering Attacks

The purpose of this review article is to determine approaches to solving existing problems in taking into account the psychological aspects of an organization’s information security in the context of socio-engineering attacks based on the analysis and systematization of sources on this topic. Methods. To achieve this goal, two complementary directions were chosen. The first direction included the investigation of selected specialized journals. The second direction involved the analysis of the representation of this problem in the Scopus database over the past 20 years. Results. The analysis of the psychological aspects of the key elements of a socio-engineering attack is carried out: the knowledge and skills of the attacker, organizational conditions, the characteristics of an employee who is part of information systems and the direction of training and prevention. A model of socio-engineering attack considering psychological aspects is proposed. Conclusions. The study showed that the approaches developed now are sufficient to form the basis for the revision of personnel processes in the organization. The problem of social engineering attacks cannot be solved without the involvement of HR services in terms of changing HR processes, taking into account information security policies. The results of this study will be of interest to specialists in the field of personnel management, personnel training, information security, information technology, artificial intelligence, executives, business owners, heads of state and municipal bodies.
An Enhanced GRU Model With Application to Manipulator Trajectory Tracking

Service robots, e.g. massage robots, have attracted more and more attention in recent years and the most popular study within this field is trajectory tracking. Due to the actual demand for service robots, the solution of trajectory tracking requires fast convergence and high accuracy. In order to solve the above issues, this paper proposed an enhanced Gated recurrent unit (GRU) to deal with trajectory tracking tasks of robot manipulators. The main feature of enhanced GRU is utilizing cell states as well as various gate units to build a novel neural cell. Besides, the presented enhanced GRU resolves the problem of the general neural network model and large memory occupancy. Then the derivations about the computational process of cell state and mixed hidden state of the proposed model have been illustrated. Finally, three trajectory tracking applications, comparison, and visual simulation have verified feasibility as well as the superiority of the enhanced GRU model.
Face Recognition Using Histogram of Oriented Gradients with TensorFlow in Surveillance Camera on Raspberry Pi

The implementation of face recognition with TensorFlow deep learning uses the webcam as a surveillance camera on the Raspberry Pi, aiming to provide a sense of security to the requiring party.A frequent surveillance camera problem is that crimes are performed at certain hours, the absence of early warning features, and there is no application of facial recognition on surveillance cameras.The function of this system is to perform facial recognition on every face captured by the webcam.Use the Histogram of the Oriented Gradient (HOG) method for the extraction process of deep learning.The image that is input from the camera will undergo a gray scaling process, then it will be taken the extraction value and classified by deep learning framework with TensorFlow.The system will send notifications when faces are not recognized.Based on the analysis of the data is done, the conclusion that the implementation of face recognition is built on the Raspberry Pi using a Python programming language with the help of TensorFlow so that the training process of the sample is much faster and more accurate.It uses a Graphical User Interface (GUI) as the main display and is built using Python designer, using email as an initial warning delivery medium to the user as well as using the webcam as the main camera to capture image.
Automated English Speech Recognition Using Dimensionality Reduction with Deep Learning Approach

Speech recognition technology is a multidisciplinary field, comprising signal processing, pattern recognition, acoustics, artificial intelligence, etc. Presently, speech recognition plays a vital role in human-computer interface in information technology. Due to the advancements of deep learning (DL) models, speech recognition system has received significant attention among researchers in several areas of speech recognition like mobile communication, voice recognition, and personal digital assistance. This paper presents an automated English speech recognition using dimensionality reduction and deep learning (AESR-DRDL) approach. The proposed AESR-DRDL technique involves a series of operations, namely, feature extraction, preprocessing, dimensionality reduction, and speech recognition. During feature extraction process, a hybridization of high-dimension rich feature vectors is derived from the speech as well as glottal-waveform signals by the use of MFCC, PLPC, and MVDR techniques. Besides, the high dimensionality of features can be reduced by the design of quasioppositional poor and rich optimization algorithm (QOPROA). Moreover, the Bidirectional Long Short-Term Memory (BiLSTM) technique is employed for speech recognition, and the optimal hyperparameter tuning of the Bidirectional Long Short-Term Memory technique can be chosen using Adagrad optimizer. For the dimensionality reduction technique, the quasioppositional poor and rich optimization algorithm (QOPROA) is applied. The performance validation of the AESR-DRDL technique is carried out against benchmark datasets, and the results reported the better performance of the AESR-DRDL technique compared to recent approaches. The AESR-DRDL technique has shown to be superior in terms of recovery time, with an average of 0.50 days. The AESR-DRDL method's overall performance has been validated using benchmark datasets, and the results show that it outperforms more current technique. Because of this, the AESR-DRDL approach can be used to recognize English speech.
The influence of tutoring and learning motivation on mathematics achievement of junior high school students

Many factors influence student academic achievement, including tutoring and learning motivation. This research aimed to describe the influence of tutoring and learning motivation on Year 8 students’ mathematics achievement in one of the junior high schools in Ruteng, Indonesia in the academic year of 2019/2020. This descriptive research with survey design involved 66 students. Data were collected by administering a student questionnaire consisting of 20 questions. The 5-point Likert scale questionnaire used in this research focused on two categories: tutoring and learning motivation. The final semester test scores were used as the data on students' achievement in learning mathematics. The results revealed that the two factors were positively related to students' mathematics achievement; both contributed to student achievement by 18.49%. These findings confirm that tutoring and students’ motivation from teachers or parents are essential because both mediate student achievement. These results provide meaningful knowledge about the importance of teachers or parents providing structured tutoring to support the mathematics achievement of junior high school students.
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs

The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but they are assumed to be collected only from several anomaly types, leaving the majority of anomaly types not represented in the collected anomaly dataset at all. To effectively leverage this kind of incomplete anomalous knowledge represented by the collected anomalies, we propose to learn a probability distribution that can not only model the normal samples, but also guarantee to assign low density values for the collected anomalies. To this end, an anomaly-aware generative adversarial network (GAN) is developed, which, in addition to modeling the normal samples as most GANs do, is able to explicitly avoid assigning probabilities for collected anomalous samples. Moreover, to facilitate the computation of anomaly detection criteria like reconstruction error, the proposed anomaly-aware GAN is designed to be bidirectional, attaching an encoder for the generator. Extensive experimental results demonstrate that our proposed method is able to effectively make use of the incomplete anomalous information, leading to significant performance gains comparing to existing methods.
Contrastive-mixup Learning for Improved Speaker Verification

This paper proposes a novel formulation of prototypical loss with mixup for speaker verification. Mixup is a simple yet efficient data augmentation technique that fabricates a weighted combination of random data point and label pairs for deep neural network training. Mixup has attracted increasing attention due to its ability to improve robustness and generalization of deep neural networks. Although mixup has shown success in diverse domains, most applications have centered around closed-set classification tasks. In this work, we propose contrastive-mixup, a novel augmentation strategy that learns distinguishing representations based on a distance metric. During training, mixup operations generate convex interpolations of both inputs and virtual labels. Moreover, we have reformulated the prototypical loss function such that mixup is enabled on metric learning objectives. To demonstrate its generalization given limited training data, we conduct experiments by varying the number of available utterances from each speaker in the VoxCeleb database. Experimental results show that applying contrastive-mixup outperforms the existing baseline, reducing error rate by 16% relatively, especially when the number of training utterances per speaker is limited.
DuMLP-Pin: A Dual-MLP-Dot-Product Permutation-Invariant Network for Set Feature Extraction

Existing permutation-invariant methods can be divided into two categories according to the aggregation scope, i.e. global aggregation and local one. Although the global aggregation methods, e. g., PointNet and Deep Sets, get involved in simpler structures, their performance is poorer than the local aggregation ones like PointNet++ and Point Transformer. It remains an open problem whether there exists a global aggregation method with a simple structure, competitive performance, and even much fewer parameters. In this paper, we propose a novel global aggregation permutation-invariant network based on dual MLP dot-product, called DuMLP-Pin, which is capable of being employed to extract features for set inputs, including unordered or unstructured pixel, attribute, and point cloud data sets. We strictly prove that any permutation-invariant function implemented by DuMLP-Pin can be decomposed into two or more permutation-equivariant ones in a dot-product way as the cardinality of the given input set is greater than a threshold. We also show that the DuMLP-Pin can be viewed as Deep Sets with strong constraints under certain conditions. The performance of DuMLP-Pin is evaluated on several different tasks with diverse data sets. The experimental results demonstrate that our DuMLP-Pin achieves the best results on the two classification problems for pixel sets and attribute sets. On both the point cloud classification and the part segmentation, the accuracy of DuMLP-Pin is very close to the so-far best-performing local aggregation method with only a 1-2% difference, while the number of required parameters is significantly reduced by more than 85% in classification and 69% in segmentation, respectively. The code is publicly available on https://github.com/JaronTHU/DuMLP-Pin.
Instance-Aware Multi-Object Self-Supervision for Monocular Depth Prediction

This paper proposes a self-supervised monocular image-to-depth prediction framework that is trained with an end-to-end photometric loss that handles not only 6-DOF camera motion but also 6-DOF moving object instances. Self-supervision is performed by warping the images across a video sequence using depth and scene motion including object instances. One novelty of the proposed method is the use of the multi-head attention of the transformer network that matches moving objects across time and models their interaction and dynamics. This enables accurate and robust pose estimation for each object instance. Most image-to-depth predication frameworks make the assumption of rigid scenes, which largely degrades their performance with respect to dynamic objects. Only a few SOTA papers have accounted for dynamic objects. The proposed method is shown to outperform these methods on standard benchmarks and the impact of the dynamic motion on these benchmarks is exposed. Furthermore, the proposed image-to-depth prediction framework is also shown to be competitive with SOTA video-to-depth prediction frameworks.
Lite Unified Modeling for Discriminative Reading Comprehension

As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials.However, the focuses of various discriminative MRC tasks may be diverse enough: multi-choice MRC requires model to highlight and integrate all potential critical evidence globally; while extractive MRC focuses on higher local boundary preciseness for answer extraction.Among previous works, there lacks a unified design with pertinence for the overall discriminative MRC tasks.To fill in above gap, we propose a lightweight POS-Enhanced Iterative Co-Attention Network (POI-Net) as the first attempt of unified modeling with pertinence, to handle diverse discriminative MRC tasks synchronously.Nearly without introducing more parameters, our lite unified design brings model significant improvement with both encoder and decoder components.The evaluation results on four discriminative MRC benchmarks consistently indicate the general effectiveness and applicability of our model, and the code is available at https://github.com/Yilin1111/poi-net.
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking

Dialogue state tracking (DST) aims to extract essential information from multi-turn dialogue situations and take appropriate actions.A belief state, one of the core pieces of information, refers to the subject and its specific content, and appears in the form of domain-slot-value.The trained model predicts "accumulated" belief states in every turn, and joint goal accuracy and slot accuracy are mainly used to evaluate the prediction; however, we specify that the current evaluation metrics have a critical limitation when evaluating belief states accumulated as the dialogue proceeds, especially in the most used MultiWOZ dataset.Additionally, we propose relative slot accuracy to complement existing metrics.Relative slot accuracy does not depend on the number of predefined slots, and allows intuitive evaluation by assigning relative scores according to the turn of each dialogue.This study also encourages not solely the reporting of joint goal accuracy, but also various complementary metrics in DST tasks for the sake of a realistic evaluation.
Noise thresholds for classical simulability of nonlinear boson sampling

Boson sampling, a computational problem conjectured to be hard to simulate on a classical machine, is a promising candidate for an experimental demonstration of quantum advantage using bosons. However, inevitable experimental noise and imperfections, such as loss in the interferometer and random counts at the detectors, could challenge the sampling task from entering the regime where quantum advantage is achievable. In this work we introduce higher-order nonlinearities as a means to enhance the computational complexity of the problem and the protocol's robustness against noise, i.e., to increase the noise threshold that allows us to perform an efficient classical simulation of the problem. Using a phase-space method based on the negativity volume of the relevant quasiprobability distributions, we establish a necessary nonclassicality condition that any experimental proof of quantum advantage must satisfy. Our results indicate that the addition of single-mode Kerr nonlinearity at the input-state preparation level, while retaining a linear-optical evolution, makes the boson-sampling protocol more robust against noise and consequently relaxes the constraints on the noise parameters required to show quantum advantage.
Satellite-based quantum information networks: use cases, architecture, and roadmap

Quantum Information Networks (QINs) attract increasing interest, as they enable connecting quantum devices over long distances, thus greatly enhancing their intrinsic computing, sensing, and security capabilities. The core mechanism of a QIN is quantum state teleportation, consuming quantum entanglement, which can be seen in this context as a new kind of network resource. Here we identify use cases per activity sector, including key performance targets, as a reference for the network requirements. We then define a high-level architecture of a generic QIN, before focusing on the architecture of the Space segment, with the aim of identifying the main design drivers and critical elements. A survey of the state-of-the-art of these critical elements is presented, as are issues related to standardisation. Finally, we explain our roadmap to developing the first QINs and detail the already concluded first step, the design and numerical simulation of a Space-to-ground entanglement distribution demonstrator.
Simple genetic operators are universal approximators of probability distributions (and other advantages of expressive encodings)

This paper characterizes the inherent power of evolutionary algorithms. This power depends on the computational properties of the genetic encoding. With some encodings, two parents recombined with a simple crossover operator can sample from an arbitrary distribution of child phenotypes. Such encodings are termed expressive encodings in this paper. Universal function approximators, including popular evolutionary substrates of genetic programming and neural networks, can be used to construct expressive encodings. Remarkably, this approach need not be applied only to domains where the phenotype is a function: Expressivity can be achieved even when optimizing static structures, such as binary vectors. Such simpler settings make it possible to characterize expressive encodings theoretically: Across a variety of test problems, expressive encodings are shown to achieve up to super-exponential convergence speed-ups over the standard direct encoding. The conclusion is that, across evolutionary computation areas as diverse as genetic programming, neuroevolution, genetic algorithms, and theory, expressive encodings can be a key to understanding and realizing the full power of evolution.
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

Nucleus segmentation and classification within the Haematoxylin and Eosin stained histology images is a key component in computer-aided image analysis, which helps to extract features with rich information for cellular estimation and following diagnosis. Therefore, it is of great relevance for several downstream computational pathology applications In this work, we address the problem of automatic nuclear segmentation and classification. Our solution is to cast as a simultaneous semantic and instance segmentation framework, and it is part of the Colon Nuclei Identification and Counting (CoNIC) Challenge. Our framework is a carefully designed ensemble model. We first train a semantic and an instance segmentation model separately, where we use as backbone HoverNet and Cascade Mask-RCNN models. We then ensemble the results with a customized Non-Maximum Suppression embedding algorithm. From our experiments, we observe that the semantic segmentation part can achieve an accurate class prediction for the cells whilst the instance information provides a refined segmentation. We enforce a robust segmentation and classification result through our customized embedding algorithm. We demonstrate, through our visual and numerical experimental, that our model outperforms the provided baselines by a large margin. Our solution ranked as the $$4^{th}$$ solution on the Grand Challenge CoNIC 2022.
Task-guided Disentangled Tuning for Pretrained Language Models

Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically finetuned on task-specific downstream datasets, which have produced state-of-the-art results on various NLP tasks.However, the data discrepancy issue in domain and scale makes finetuning fail to efficiently capture task-specific patterns, especially in the low data regime.To address this issue, we propose Task-guided Disentangled Tuning (TDT) for PLMs, which enhances the generalization of representations by disentangling task-relevant signals from the entangled representations.For a given task, we introduce a learnable confidence model to detect indicative guidance from context, and further propose a disentangled regularization to mitigate the over-reliance problem.Experimental results on GLUE and CLUE benchmarks show that TDT gives consistently better results than fine-tuning with different PLMs, and extensive analysis demonstrates the effectiveness and robustness of our method.Code is available at https://github.com/lemon0830/TDT.