Reviewerly/exHarmony huggingface dataset:
Out-of-distribution Detection with Boundary Aware Learning

There is an increasing need to determine whether inputs are out-of-distribution (OOD) for safely deploying machine learning models in the open world scenario. Typical neural classifiers are based on the closed world assumption, where the training data and the test data are drawn i.i.d. from the same distribution, and as a result, give over-confident predictions even faced with OOD inputs. For tackling this problem, previous studies either use real outliers for training or generate synthetic OOD data under strong assumptions, which are either costly or intractable to generalize. In this paper, we propose boundary aware learning (BAL), a novel framework that can learn the distribution of OOD features adaptively. The key idea of BAL is to generate OOD features from trivial to hard progressively with a generator, meanwhile, a discriminator is trained for distinguishing these synthetic OOD features and in-distribution (ID) features. Benefiting from the adversarial training scheme, the discriminator can well separate ID and OOD features, allowing more robust OOD detection. The proposed BAL achieves state-of-the-art performance on classification benchmarks, reducing up to 13.9% FPR95 compared with previous methods.
Grid investment capability prediction based on path analysis and BP neural network

With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability.
Differential Privacy Technology of Big Data Information Security based on ACA-DMLP

Cloud computing and artificial intelligence have a deeper and closer connection with daily life.To ensure information security, most companies or individuals choose to pay a simple fee to store a large amount of data on cloud servers and hand over a large number of complex calculations of machine learning to cloud servers.To eliminate the security risks of data stored in the cloud and ensure that private data is not leaked, this paper proposes a collusion-resistant distributed machine learning scheme.Through homomorphic encryption algorithm and differential privacy algorithm, the security of data and model in machine learning framework is guaranteed.The distributed machine learning framework is adopted to reduce the data computing time and improve the data training efficiency.The simulation results show that the computational efficiency is improved while the user privacy security is guaranteed.The accuracy of model training is not reduced due to the improvement of privacy data security and computational efficiency.Through this study, we can further propose effective measures for the privacy protection of outsourced data and the data integrity of machine learning, which is of great significance to the security research of cloud intelligent big data.
Para a construção de um corpus de interações orais em Português Língua Não Materna (PLNM) algumas reflexões

authentic data sets, different types of corpora increasingly support theoretical and applied linguistic studies. In the same vein, corpora may also constitute an appropriate working basis for second language acquisition and foreign language teaching, especially with regard to the uses of language. However, both Pragmatics, as a discipline focused on the analysis of meaning in context and the area of second language acquisition (SLA) have been quite reticent when it comes to using corpora, whether written or oral. In this text, we present a characterization of spoken learner corpora and advocate their relevance and usefulness to the teaching and learning of Portuguese as a Foreign Language, namely in what concerns Interlanguage Pragmatics. In the same line of thinking, the PL2 oral interaction corpus project presented in this text, with the challenges faced by its implementation, and the possibilities for future research that it will allow, aims at contributing to a better understanding of the scientific and pedagogical advantages of a database containing texts of a more interactive nature
Comprehensive comparisons of embedding approaches for cryptographic API completion

In this paper, we conduct a measurement study to comprehensively compare the accuracy of Cryptographic API completion tasks trained with multiple API embedding options. Embedding is the process of automatically learning to represent program elements as low-dimensional vectors. Our measurement aims to uncover the impacts of applying program analysis, token-level embedding, and sequence-level embedding on the Cryptographic API completion accuracies. Our findings show that program analysis is necessary even under advanced embedding. The results show 36.10% accuracy improvement on average when program analysis preprocessing is applied to transfer byte code sequences into API dependence paths. The best accuracy (93.52%) is achieved on API dependence paths with embedding techniques. On the contrary, the pure data-driven approach without program analysis only achieves a low accuracy (around 57.60%), even after the powerful sequence-level embedding is applied. Although sequence-level embedding shows slight accuracy advantages (0.55% on average) over token-level embedding in our basic data split setting, it is not recommended considering its expensive training cost. A more obvious accuracy improvement (5.10%) from sequence-level embedding is observed under the cross-project learning scenario when task data is insufficient. Hence, we recommend applying sequence-level embedding for cross-project learning with limited task-specific data.
DACE: Did I Catch You at a Good Time?

Social Information Seeking (SIS) is a platform for easy and informal interaction among participants. This has been popularized by many of the major prevailing Community Question Answering (CQA) forums, such as Quora, Stackoverflow, etc. A user submits a question to the forum that is later answered by one or more of the users in the forum. These sites facilitate knowledge exchange between users through questions and answers. A large pool of knowledge has since been created. Although hundreds of questions are posted every hour, many of them remain unanswered due to lower quality, wrong choice of question tags, bad routing of questions to experts, and dearth of proper answerers. In this paper, we propose a solution to the problem of routing questions to appropriate experts by utilizing the tag and time information of the posted questions. We propose a recommendation model that uses a tag network and temporal analysis to find the potential experts. Our experiments over a dataset from Stack Exchange show the effectiveness of our approach over several baseline models.
A Multi-class image reranking algorithm based on multiple discrete-time quantum walk

To achieve multi-class image reranking, a novel image reranking algorithm using multiple discrete-time quantum walk is proposed. In this algorithm, a weighted undirected complete graph is first constructed, in which the nodes for the graph represent the images and the weighted values of these edges are the similarity value between the images. Secondly, it uses the spectral clustering to divide the images into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> classes and finds the representative image of each class. Thirdly, it uses the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> representative images as the initial state of quantum system, and the flip-flop shift operator and the weighted coin operator are used to control multiple discrete-time quantum walk on the weighted complete graph. Finally, the average probability values of the walker reaching the node of the graph is used as the relevance scores of the image, and then the images are reranked by the relevance scores. the experimental results show that our scheme has a significant enhance compared with the initial ranking algorithm from the comparison of visual and relevance scores. Furthermore, the effectiveness of our algorithm is evaluated by the average precision (AP) and the mean average precision (MAP), where the AP of our algorithm is increased by 53.21%, 31.75% and 14.29% for three types of the query image in randomly selected image group respectively, and the MAP of our algorithm is increased by 29.57% for all image groups compared with the initial ranking algorithm.
Design of smart wearable Bracelet based on Android mobile phone

Nowadays, the population is gradually aging, and people pay more and more attention to the health of the elderly. In this paper, a wearable bracelet is designed by using wearable technology, signal processing and Bluetooth communication technology. The device can obtain the body posture and body parameters of the elderly in real time, and understand the health status of the elderly. The experimental results show that the device can accurately detect human motion posture, including sleep posture, walking or running state, fall and human heart rate parameters, and the error of heart rate parameters is within ± 2 times, which can provide help for the daily health monitoring of the elderly.
Preference-Aware Modality Representation and Fusion for Micro-video Recommendation

Personalized multi-modal micro-video recommendation has attracted increasing research interests recently. Despite existing methods have achieved much progress, they ignore the importance of the user’s modality preference for micro-video recommendation. In this paper, we explore the preference-aware modality representation learning and dynamic modality information fusion, and respectively present coarse- and fine-grained modeling approaches for each aspect. Moreover, we conduct extensive experiments by integrating these approaches into existing recommendation systems, and the results demonstrate that our proposed methods can significantly improve their performance.
Implementation of Public Information Disclosure in Creating Good Governance at Universitas Andalas

Implementation of Public Information Disclosure in Creating Good Governance at Andalas University. In line with the demands for reform, good governance comes in the form of a new transplant which is expected to be able to take care of the bureaucracy which has been represented as a bureaucracy full of acts of corruption and abuse of power. Transparency is an important aspect of governance that demands an increasingly open and transparent government system. The enactment of the Information Disclosure Act is one aspect to increase public oversight of the government system. Writing this journal is intended to be able to provide an overview by analyzing the implementation of public information disclosure in realizing good governance at Andalas University as a government institution that is to provide the information needed in the implementation of higher education. This study uses descriptive qualitative methods, while for data collection techniques in this research study the authors conducted interviews, and documentation. The results of the study indicate that the implementation of Public Information Disclosure in Creating Good Governance at Andalas University is still not running optimally, this is caused by communication that does not go well, inadequate resources.
Some Issues of the Development of Teachers' Adaptive Competence

The article discusses the issue of flexibility competence of teachers in the process of continuing education. Today, when school education and the role of the teacher are being renewed, teachers are required not only to teach in the classroom, but also to meet the special needs of students. Therefore, teachers today need to have competencies such as flexibility, including a evidence-based approach that allows them to adapt their activities to the new needs of students
Co-simulation platform for geometric design, trajectory control and guidance of racing drones

The design of racing drones brings quite a thrilling challenge from a flight dynamics point of view. This work aims to offer a single-based simulation platform combining its geometric design, trajectory control, and guidance of racing drones. Also, it is reckoned from a pilot’s view in a classic FPV competition. Hence, it is an active platform for studying racing drones’ design founded on dynamics, with fifteen different drone models. It is one of the few existing platforms that combine all aspects of racing drones in a single simulation environment. Also, it is open access via Matlab Central - File Exchange.
An Improved Object Detection Algorithm for Assembly Line Garbage Sorting

Garbage object detection is of central importance to achieve accurate garbage sorting. In this work, we propose an improved object detection algorithm for sorting complex garbage objects on the assembly line. This algorithm utilizes the Faster RCNN as the baseline and improves its performance with several tips, including deformable convolution, PAFPN, mixup, cutout, color jitter, multi-scale training and ImageNet transfer learning. Considering the lack of public datasets for the garbage object detection task, we synthesize a highly realistic multi-object garbage detection dataset, M-GarbageNet, with 20000 training images, 5000 validation images and 5000 test images. Finally, we evaluate our algorithm than Faster RCNN, and obtain a 8.2% relative improvement on the M-GarbageNet dataset.
A Small GIFT-COFB: Lightweight Bit-Serial Architectures

GIFT-COFB is a lightweight AEAD scheme and a submission to the ongoing NIST lightweight cryptography standardization process where it currently competes as a finalist. The construction processes 128-bit blocks with a key and nonce of the same size and has a small register footprint, only requiring a single additional 64-bit register. Besides the block cipher, the mode of operation uses a bit permutation and finite field multiplication with different constants. It is a well-known fact that implementing a hardware block cipher in a bit-serial manner, which advances only one bit in the computation pipeline in each clock cycle, results in the smallest circuits. Nevertheless, an efficient bit-serial circuit for a mode of operation that utilizes finite field arithmetic with multiple constants has yet to be demonstrated in the literature. In this paper, we fill this gap regarding efficient field arithmetic in bit-serial circuits, and propose a lightweight circuit for GIFT-COFB that occupies less than 1500 GE, making it the to-date most area-efficient implementation of this construction. In a second step, we demonstrate how the additional operations in the mode can be executed concurrently with GIFT itself so that the total latency is significantly reduced whilst incurring only a modest area increase. Finally, we propose a first-order threshold implementation of GIFT-COFB, which we experimentally verify resists first-order side-channel analysis. (For the sake of reproducibility, the source code for all proposed designs is publicly available [14]).
Seminaïve Materialisation in DatalogMTL

DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream reasoning. Practical algorithms for DatalogMTL are reliant on materialisation-based reasoning, where temporal facts are derived in a forward chaining manner in successive rounds of rule applications. Current materialisation-based procedures are, however, based on a naïve evaluation strategy, where the main source of inefficiency stems from redundant computations. In this paper, we propose a materialisation-based procedure which, analogously to the classical seminaïve algorithm in Datalog, aims at minimising redundant computation by ensuring that each temporal rule instance is considered at most once during the execution of the algorithm. Our experiments show that our optimised seminaïve strategy for DatalogMTL is able to significantly reduce materialisation times.
Bdsl47: A Complete Open-Access Depth-Based Bangla Sign Alphabet and Digit Dataset

Sign Language Recognition targets on interpreting the sign language into text or speech, in order to facilitate the communication between deaf-mute people and ordinary people. Constructing a sign language dataset has broad social impact, but is challenging due to its complexity and large variations in hand actions. In Bangla Sign Language (BdSL), not many of such datasets are available online, and the existing ones are based on RGB images, whereas, having a depth dataset to incorporate depth information during the classification phase can significantly increase the recognition accuracy. In this paper, we have proposed a complete depth-based open-access dataset for 47 one-handed static signs (10 digits, from ০ to ৯; and 37 letters, from অ to ँ) of Bangla Sign Language, using the MediaPipe framework for extracting depth information. For classification, we primarily used baseline classifiers, and later we designed an Artificial Neural Network (ANN) model that resulted in an accuracy of 97.78%. Moreover, we have compared the Bangla sign alphabet dataset with an existing benchmark dataset ‘Ishara Lipi’ using deep learning based approach and achieved 98.92% accuracy, which is significantly higher than the Ishara Lipi dataset.
MHPro: Multi-hypothesis Probabilistic Modeling for Human Mesh Recovery

Recovering 3D human meshes from monocular images is an inherently ambiguous and challenging task due to depth ambiguity, joint occlusion and truncation. However, most recent works avoid modeling uncertainty, typically obtaining a single reconstruction for a given input. In contrast, this paper presents the ambiguity of reception reconstruction and considers the problem as an inverse problem for which multiple feasible solutions exist. Our method, MHPro, first constructs a probability distribution and obtains a set of feasible recovery results (i.e. multi-hypotheses), from monocular images. Intra-hypothesis refinement is then performed to achieve independent feature enhancement. Finally, the multi-hypothesis features are aggregated by inter-hypothesis communication to recover the final 3D human mesh. The effectiveness of our method is validated on two benchmark datasets, Human3.6M and 3DPW, where experimental results show that our method achieves state-of-the-art performance and recovers more accurate human meshes. Our results validate the importance of intra-hypothesis refinement and inter-hypothesis communication in probabilistic modeling and show optimal performance across a variety of settings. Our source code will be available at http://cic.tju.edu.cn/faculty/likun/projects/MHPro .
Machine Learning Approaches for Image-Based Screening of Cervical Cancer

Cervical cancer can be prevented if it is diagnosed at an early stage using screening methods. The need for automated cancer screening methods is emphasized due to the tedious task of cytotechnicians and medical experts, who spend hours analyzing the cellular abnormalities under the microscope. The objective of this review paper is to gain awareness from the recent researches on automated screening methods using image processing techniques, classical machine learning, and more recent deep learning approaches. The paper primarily focuses on the classification of cervical cell types, both binary and multiclass. It also provides a comprehensive understanding of the datasets and the performance metrics used in these studies. It targets cancer studies promote more focused researches to be undertaken in the future. The recent literature shows that deep-learning-based methods are quite popular in delivering promising results in image-based classification and segmentation tasks in diagnosing cervical cancer. The development of such automated systems can assist doctors or pathologists in a much quicker error-free decision-making process.
S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction

In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. The architecture of S2F2 consists of two primary parts: (1) a context feature extractor responsible for extracting a shared latent feature embedding for performing detection and Re-ID, and (2) a forecasting module responsible for extracting a shared latent feature embedding for forecasting. The outputs of the two parts are then processed to generate the final predicted trajectories of pedestrians. Unlike previous approaches, the computational burden of S2F2 remains consistent even if the number of pedestrians grows. In order to fairly compare S2F2 against the other approaches, we designed a StaticMOT dataset that excludes video sequences involving egocentric motions. The experimental results demonstrate that S2F2 is able to outperform two conventional trajectory forecasting algorithms and a recent learning-based two-stage model, while maintaining tracking performance on par with the contemporary MOT models.
Research on Named Entity Recognition Method of Network Threat Intelligence

Abstract With the continuous emergence of new network threat means, how to turn passive defense into active prediction, the rise of Cyber Threat Intelligence (CTI) technology provides a new idea. CTI technology can timely and effectively obtain all kinds of network security threat intelligence information to help security personnel quickly identify all kinds of attacks and make effective decisions in time. However, there are not only a large number of redundant information in threat intelligence information, but also the problems of Chinese English mixing, fuzzy boundary, and polysemy of related security entities. Therefore, identifying complex and valuable information from this information has become a great challenge. Through the research on the above problems, a named entity recognition model in the field of Network Threat Intelligence Based on BERT-BiLSTM-Self-Attention-CRF is proposed to identify the complex network threat intelligence entities in the text. Firstly, the dynamic word vector is obtained through Bert to fully represent the semantic information and solve the problem of polysemy of a word. Then the obtained word vector is used as the input of BiLSTM, and the context feature vector is obtained by BiLSTM. Then the output result is introduced into the self-attention mechanism to capture the correlation within the data or features, and finally the result is input into CRF for annotation. To verify the effectiveness of the model, experiments are carried out on the constructed network threat intelligence data set. The results show that the model significantly improves the effect of Threat Intelligence named entity recognition compared with several other classical models.