Reviewerly/exHarmony huggingface dataset:
Morphology-Based Soft Label Smoothing Strategy for Fine-Grained Domain Adaptationming

Semantic segmentation algorithms are the cornerstone of the autonomous driving algorithm. The implementation of these algorithms requires a lot of data. However, due to the complex and changeable production environment, the differences in image style, illumination and weather conditions not only significantly degrade the performance of network models under development environment data, but also cause costly data annotation. This paper proposes a dilation-corrosion soft label smoothing strategy. This strategy utilizes the existing self-supervised learning soft label generation strategy and overcomes the premise assumption defect of the fine-grained adversarial model on the target domain model. Our strategy evaluates its effectiveness on the domain adaptation task of GTA5 → Cityscapes. At the same time, we reduce the amount of source domain data by half and the number of domain classification training iterations by half, the resulting in the comprehensive evaluation index is 1.5% higher, and the individual category is 10 percentage points higher. Our strategy shortens the time and improves the efficiency of segmentation.
Knowledge Enhanced BERT Based on Corpus Associate Generation

The pre-training model represented by BERT has limited accuracy due to a lack of professional domain knowledge support. The knowledge-enhanced BERT model effectively improves the lack of knowledge in downstream tasks in different fields by introducing external professional knowledge and has achieved better results than the BERT model. However, the knowledge-enhanced BERT injected large-scale external knowledge, resulting in a sharp increase in computing resource requirements and training time. To solve this problem, a knowledge enhancement method based on corpus associate generation (CAG) is proposed. The input text corpus is disassembled into a sequential sequence of word sets and entity noun sets through entity noun recognition, and the word sets are associated with external knowledge triples through entity nouns. Using semantic splicing to generate corpus with external knowledge. Finally, the original BERT model structure is used to inject the corpus containing knowledge into the downstream tasks for knowledge enhancement, avoiding the introduction of additional model structures to increase the model complexity, to achieve the purpose of reducing the amount of calculation. Experimental analysis with 6 public data sets such as LCQMC and XNLI, the results show that compare to K-BERT, the average calculation time of knowledge introduction and training is reduced by 53.5% and 37.4%.
A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on Machine Learning

In order to accurately identify the sensitive information in the intangible cultural heritage communication network and realize the reasonable protection of intangible cultural heritage data, a method for protecting the sensitive information in the intangible cultural heritage communication network based on machine learning is proposed. With the support of machine learning algorithm, the distance measurement results are solved, and then the specific values of compressed characteristic indexes are calculated by establishing a random measurement matrix to complete the tracking and processing of the target parameters of intangible cultural heritage. On this basis, according to the encryption processing results of sensitive information, the implementation standard of OSBE protocol is established, and then with the help of the formed sensitive information processing process, the effective protection of sensitive information of intangible cultural heritage communication network is realized. The results of comparative experiments show that under the effect of machine learning algorithm, the recognition accuracy of the network host for the sensitive information of intangible cultural heritage has significantly improved, and it really has strong practical value in the reasonable protection of intangible cultural heritage data parameters.
Weakening economy will undermine Tunisia’s president

Headline TUNISIA: Weakening economy will undermine president
Medical voice question: answering interactive system based on speech recognition technology

The traditional medical voice question and answer interactive system can not meet the core needs of patient consultation service, and the patient's satisfaction with the system answer is low, so the research of medical voice question and answer interactive system based on speech recognition technology is proposed. In the hardware of the system, the control core of the system is the MCU module, which controls the normal operation of the whole system, realizes the conversion of the analog signal and digital signal through the audio input device, designs the serial communication device to transmit the data information to the computer, and uses the speech recognition technology to realize the software part of the system. And through the system test, the medical voice question and answer interactive system based on speech recognition technology is compared with the traditional medical question and answer interactive system. The experimental results show that the average response time of the medical voice question and answer interaction system based on speech recognition technology to the user request is far less than the traditional system, there is no number of error requests, and the performance is better than the traditional system. Therefore, it can be proved that the performance of the medical question and answer interactive system based on speech recognition technology has reached the expected standard, and can meet the basic needs of users for medical consultation services
Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks

With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency.
Gaussian Mean Testing Made Simple

We study the following fundamental hypothesis testing problem, which we term Gaussian mean testing. Given i.i.d. samples from a distribution p on ℝd, the task is to distinguish, with high probability, between the following cases: (i) p is the standard Gaussian distribution, N(0, Id), and (ii) p is a Gaussian N(μ, Σ) for some unknown covariance Σ and mean μ ∈ ℝd satisfying ||μ||2 ≥ ε. Recent work gave an algorithm for this testing problem with the optimal sample complexity of . Both the previous algorithm and its analysis are quite complicated. Here we give an extremely simple algorithm for Gaussian mean testing with a one-page analysis. Our algorithm is sample optimal and runs in sample linear time.* Authors are in alphabetical order. The full version of the paper can be accessed at https://arxiv.org/abs/2210.13706
Faster Walsh-Hadamard Transform and Matrix Multiplication over Finite Fields using Lookup Tables

We use lookup tables to design faster algorithms for important algebraic problems over finite fields. These faster algorithms, which only use arithmetic operations and lookup table operations, may help to explain the difficulty of determining the complexities of these important problems. Our results over a constant-sized finite field are as follows.The Walsh-Hadamard transform of a vector of length N can be computed using O(N log N/ log log N) bit operations. This generalizes to any transform defined as a Kronecker power of a fixed matrix. By comparison, the Fast Walsh-Hadamard transform (similar to the Fast Fourier transform) uses O(N log N) arithmetic operations, which is believed to be optimal up to constant factors.Any algebraic algorithm for multiplying two N × N matrices using O(Nω) operations can be converted into an algorithm using O(Nω/(log N)ω/2−1) bit operations. For example, Strassen's algorithm can be converted into an algorithm using O(N2.81/(log N)0.4) bit operations. It remains an open problem with practical implications to determine the smallest constant c such that Strassen's algorithm can be implemented to use c · N2.81 + o(N2.81) arithmetic operations; using a lookup table allows one to save a super-constant factor in bit operations.* The full version of the paper can be accessed at https://arxiv.org/abs/2211.04643
Analyzing and Implementing GPU Hash Tables

We revisit the problem of building static hash tables on the GPU and present an efficient implementation of bucketed hash tables. By decoupling the probing scheme from the hash table in-memory representation, we offer an implementation where the number of probes and the bucket size are the only factors limiting performance. Our analysis sweeps through the hash table parameter space for two probing schemes: cuckoo and iceberg hashing. We show that a bucketed cuckoo hash table (BCHT) that uses three hash functions outperforms alternative methods that use iceberg hashing and a cuckoo hash table that uses a bucket size of one. At load factors as high as 0.99, BCHT enjoys an average probe count of 1.43 during insertion. Using three hash functions only, positive and negative queries require at most 1.39 and 2.8 average probes per key, respectively.
Two-Stage High Precision Membership Inference Attack

Most membership inference attacks (MIA) identify training set records by observing the particular behavior of machine learning models on training data, but these methods based only on overfitting are difficult to achieve high precision. Even though recent difficulty calibration techniques have alleviated this problem, calibrated attacks can still only identify a smaller number of memberships with high precision. In this work, we rethink the value of overfitting for MIA and we argue that overfitting can provide clear signals of non-membership to the adversary. In scenarios where the cost of an attack is high, such signals can prevent the adversary from launching unnecessary attacks. We propose a simple and efficient two-stage high-precision MIA that uses an overfitting-based attack to perform “membership exclusion” before performing the MIA. We show that this two-stage attack can significantly increase the number of identified members while guaranteeing high precision.
A Event Extraction Method of Document-Level Based on the Self-attention Mechanism

Event extraction is an important task in the field of natural language processing. However, most of the existing event extraction techniques focus on sentence-level extraction, which inevitably ignores the contextual features of sentences and the occurrence of multiple event trigger words in the same sentence. Therefore, this paper mainly uses the multi-head self-attention mechanism to integrate text features from multiple dimensions and levels to achieve the task of event detection at the level of text. First, convolutional neural network combined with dynamic multi-pool strategy is used to extract sentence level features. Secondly, the discourse feature representation of full-text information fusion is obtained by multi-head self-attention mechanism model. Finally, using the classifier function to classify, and then detect the trigger word and category of the event. Experimental results show that the proposed method achieves good results in document-level event extraction.
UK cyber cases highlight technical and training needs

Headline UK: Cyber cases highlight technical and training needs
COMFO: Multilingual Corpus for Opinion Mining

The use of Machine Learning (ML) algorithms in opinion mining, particularly supervised learning algorithms, requires an annotated corpus to train the classification model in order to predict results that are close to reality. Unfortunately, there are still no resources for the automatic processing of textual data expressed in the Senegalese urban language. The objective of this paper is to build a multilingual corpus for opinion mining (COMFO). The process of building the COMFO corpus is composed of three steps: presentation of the data source, data collection and preparation, and annotation by lexical approach. The particularity of COMFO lies in the integration of foreign languages (French and English) and local languages, particularly urban Wolof, in order to reflect the collective opinion of Senegalese readers.
Research on the Strategy of Promoting the Informatization Management of Physical Education Teaching in Colleges and Universities

Carrying out teaching informatization construction and management in physical education in colleges and universities can focus on improving the teaching efficiency of physical education, help physical education achieve resource sharing, and accelerate the pace of building an internal education platform on digital campus.Up to now, the informatization construction and management of physical education teaching in colleges and universities are faced with the following problems: teachers do not have a deep understanding of the informatization of physical education teaching, lack of scientific resources for physical education informatization teaching, and imperfect supporting infrastructure for physical education informatization teaching.The informatization management level of physical education teachers needs to be improved, and there are many problems such as the lack of a sound and perfect examination system for physical education informatization teaching.The informatization construction and management of physical education teaching in colleges and universities should focus on improving the informatization management awareness of physical education teachers, increasing the development of informatization teaching resources of physical education, building an informatization teaching communication platform of physical education, and increasing the construction of informatization high-quality teachers, as well as improving the supporting evaluation system of informatization of physical education.
Design of Popular Science Knowledge Sharing Platform Based on Distance Learning

Traditional popular science knowledge sharing platform, because the data update in the sharing platform is not timely, there are malicious advertisements in the platform, and the number of users in the sharing platform is limited.Once the number of users is higher than the set value of the platform, the situation of being stuck and unable to get into the platform will occur, which fails to meet people's expectations for distance learning.Therefore, in order to facilitate more learners to carry out distance learning on the Internet platform, learners will not be restricted by age, identity and region through distance learning, and further explore its daily work flow by designing the structure of scientific knowledge sharing platform for distance learning.This paper divides the popular science knowledge sharing platform based on distance learning into two aspects: platform structure and platform workflow.The structure design of knowledge sharing platform includes resource library module, resource operation service module, external connection and output of data resources.Finally, in order to maintain the operation of the popular science knowledge sharing platform based on distance learning, each module has its own unique functions.This paper designs a simple and efficient distance learning scientific knowledge sharing platform with process task management as the core, process instance management, process privacy management, process responsibility management and other functions.
Research on Cultivation of "Foreign Language Life" Awareness of International College Students in Private Higher Vocational Colleges—Taking Guangzhou Huashang Vocational College as an Example

Under the background of "One Belt and One Road", how to effectively cultivate foreign language compound technical talents who have both basic professional application skills and master application of foreign language technology in the modern college foreign language practice teaching system in our country is also an economic reality in our country in this century.The inevitable need for talents, international college students in private higher vocational colleges are an indispensable part of higher education in our country.Combining actual work and thinking, this paper takes the international college students of Guangzhou Huashang Vocational College as an example, and proposes to plan the "foreign language life" on and off campus among the non-foreign language majors in the international college of our school, in order to cultivate the awareness of the "foreign language life" of the international college students.Specific methods include formulating English teaching plans, building a foreign language environment, encouraging online communication and learning, using foreign languages to carry out cultural or literary activities, and trying to cultivate the professional lifestyle of foreign language teachers.
The Power Source of Mathematics Teachers' Teaching Reflection

No matter traditional education or wisdom education, rationality and validity of teaching practice are eternal demands.The rationality and effectiveness of teaching practice are realized in teachers' continuous reflection.Although the social constraint of education determines that teachers' reflection needs various external reinforcement and external support.However, this paper mainly studies the four factors that push mathematics teachers to reflect on teaching, which are educational theory learning, teaching self-evaluation, "problem solving" in mathematics, dialogue and communication among teaching subjects.
Wi-Fi pings might let hackers run down your battery

You can boost well-being and achieve your personal goals by taking control of your self-narrative, finds David Robson
A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

Specific anatomical structures from the female body, such as the axillary slope, armpit, pectoral muscle, or abdominal tissue, can be present in mammograms and might affect the proper mammogram analysis, especially in female populations with overweight issues, as is the case in Mexico. This work aims to determine if better results can be obtained in an automatic mammogram analysis by removing the abdominal and axillary fatty tissues as a preprocessing step. The experimentation is carried out by applying a pectoral muscle segmentation technique in a Mexican mammogram dataset and comparing the results by removing the fatty tissues from the same mammograms. Furthermore, the same experimentation is performed using the Portuguese public datasets, INbreast and BCDR. The conducted experiment will allow us to determine the differences in results across different populations. The fat removal method is based on the breast contour-edge from which points of interest are detected to cut the fat tissue. Our results suggest that the proposed method is suitable as a preprocessing technique, obtaining a 94.18% of acceptance, according to the qualitative analysis performed, and showed that removing the fatty tissue yields better results if the mammogram contains significant fatty tissue such as in the mammograms of Mexican patients.
Advertising Image Saliency Prediction Method Based on Score Level Fusion

At present, visual saliency prediction algorithms have been developed more and more mature, but most of the current saliency prediction algorithms are aimed at natural images. Due to the inconsistency of elements and features between natural images and advertising images, the existing saliency prediction algorithms show poor robustness and low inference speed to advertising images, which severely limits its commercial application in advertising design and evaluation. In view of this, a saliency prediction algorithm for advertisement images is proposed in this paper. In the feature extraction stage, two text candidate regions based on intensity feature and improved MESR algorithm are first obtained and further integrated to produce a two-dimensional text confidence score. Meanwhile, a saliency confidence score is also obtained by an improved natural image saliency prediction network. Then, the score level fusion strategy was adopted to fuse the two confidence scores to get the final saliency prediction map. The experimental results show that the proposed model has good accuracy and robustness in advertising images, as well as the most remarkable inference speed, which can meet the demand for real-time performance of advertising image saliency prediction, leading to great practical and commercial value.
Investigating sentiments in Brazilian and German Blogs

Social interactions have changed in recent years. People post their thoughts, opinions and sentiments on social media platforms more often, through images and videos, providing a very rich source of data about population of different countries, communities, etc. Due to the increase in the amount of data on the internet, it becomes impossible to perform any analysis in a manual manner, requiring the automation of the process. In this work, we use two blog corpora that contain images and texts. Cross-Media German Blog (CGB) corpus consists of German blog posts, while Cross-Media Brazilian Blog (CBB) contains Brazilian blog posts. Both blogs have the Ground Truth (GT) of images and texts feelings (sentiments), classified according to human perceptions. In previous work, Machine Learning and lexicons technologies were applied to both corpora to detect the sentiments (negative, neutral or positive) of images and texts and compare the results with ground truth (based on subjects perception). In this work, we investigated a new hypothesis, by detecting faces and their emotions, to improve the sentiment classification accuracy in both CBB and CGB datasets. We use two methodologies to detect polarity on the faces and evaluated the results with the images GT and the multimodal GT (the complete blog using text and image). Our results indicate that the facial emotion can be a relevant feature in the classification of blogs sentiment.
Optical–electronic hybrid Fourier convolutional neural network based on super-pixel complex-valued modulation

An optical–electronic hybrid convolutional neural network (CNN) system is proposed and investigated for its parallel processing capability and system design robustness. It is regarded as a practical way to implement real-time optical computing. In this paper, we propose a complex-valued modulation method based on an amplitude-only liquid-crystal-on-silicon spatial light modulator and a fixed four-level diffractive optical element. A comparison of computational results of convolutions between different modulation methods in the Fourier plane shows the feasibility of the proposed complex-valued modulation method. A hybrid CNN model with one convolutional layer of multiple channels is proposed and trained electrically for different classification tasks. Our simulation results show that this model has a classification accuracy of 97.55% for MNIST, 88.81% for Fashion MNIST, and 56.16% for Cifar10, which outperforms models using only amplitude or phase modulation and is comparable to the ideal complex-valued modulation method.