Reviewerly/exHarmony huggingface dataset:
Histogram-Equalized Quantization for logic-gated Residual Neural Networks

Adjusting the quantization according to the data or to the model loss seems mandatory to enable a high accuracy in the context of quantized neural networks. This work presents Histogram-Equalized Quantization (HEQ), an adaptive framework for linear and symmetric quantization. HEQ automatically adapts the quantization thresholds using a unique step size optimization. We empirically show that HEQ achieves state-of-the-art performances on CFAR-10. Experiments on the STL-10 dataset even show that HEQ enables a proper training of our proposed logic-gated (OR, MUX) residual networks with a higher accuracy at a lower hardware complexity than previous work.
Ensemble Learning for Cluster Number Detection Based on Shared Nearest Neighbor Graph and Spectral Clustering

Detecting the number of clusters is important for cluster analysis. Many existing methods detect the cluster number by predefining a list of candidate cluster numbers. However, if the candidate cluster numbers are not well predefined, the cluster number cannot be correctly detected. In this paper, we propose a novel clustering method which automatically generates the candidate cluster numbers and the corresponding cluster partitions based on multiple shared nearest neighbor graphs. A shared low-rank similarity matrix is then recovered from the cluster partitions by ensemble learning. Finally, spectral clustering is applied on the shared low-rank similarity matrix with the candidate cluster numbers to detect the cluster number. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method not only correctly detects the cluster numbers, but also obtains better clustering results in comparison to the existing methods.
Cooperative Differentially Private LQG Control With Measurement Aggregation

When multiple agents solve cooperatively a joint optimal control problem, it is generally beneficial for them to coordinate their control signals. However, such strategies require that the agents share their local measurements, which may be privacy-sensitive. Motivated by this issue, this letter considers the Linear Quadratic Gaussian (LQG) control problem subject to differential privacy constraints. Differential privacy ensures that the published signals of an algorithm are not too sensitive to the data of any single participating agent. We propose a two-stage architecture for differentially private LQG control and show how to optimize it by leveraging a solution that we previously developed for the Kalman filtering problem. The first stage of this architecture is most easily implemented by a coordinator aggregating and perturbing the agents’ measurements appropriately, but it can also be implemented without a trusted aggregator by using a secure sum protocol. Numerical simulations illustrate the performance improvement of this architecture over simpler alternatives such as directly perturbing the agents’ measurements.
Online Craftsman Ordering Application Development using Waterfall Methodology

Having a plan to build or renovate a house, besides the budget, choosing a skilled craftsman is one of the determining factors. Craftsman skills are needed by customers, such as building, electronics, water, yard, mechanics, and home maintenance. By choosing the right craftsman, at least you can realize the dwelling you want. Choosing the services of a craftsman is not easy. To get a good result, it is necessary to choose the services of a skilled craftsman. The purpose of this research is to develop an application for ordering craftsmen where customers can choose skilled craftsmen by looking at the rating and performance of the craftsmen. This research also aims to help craftsmen to get customers anywhere. Because many skilled craftsmen were only known around him. The software development method used is the Waterfall method, which consists of several stages. The stages of the Waterfall method include identifying problems and needs, determining solution objectives, developing, designing applications, and verifying. Based on the results of tests conducted using the black box method, the black box method test shows that this system has no errors and can be used according to its function.
Pig Face Recognition Method Based on Sparse Representation, Transfer Learning and Weight fusionPaper

Because the pig face features of different individuals are similar and have little difference, if the pre-processed pig face data set is input into the shallow network for training, it is easy to lose the detail features, while blindly deepening the network layers will lead to sudden increase of parameters, disappearance or explosion of gradients and over-fitting. Study the pig face feature deep learning network, build 32-layer and 50-layer networks, share the use weight among residual modules, and add Batch Normalization (BN) algorithm to the residual module, which makes the deep learning network easier to converge and inhibit over-fitting. The root-based weight fusion scheme combines the sparse representation results of the original samples and the new class samples to achieve high recognition rate for small sample data sets.
FPS in action

Linear recurrence equations with constant coefficients define the power series coefficients of rational functions. However, one usually prefers to have an explicit formula for the sequence of coefficients, provided that such a formula is "simple" enough. Simplicity is related to the compactness of the formula due to the presence of algebraic numbers: "the smaller, the simpler". This poster showcases the capacity of recent updates on the Formal Power Series (FPS) algorithm, implemented in Maxima and Maple (convert/FormalPowerSeries), to find simple formulas for sequences like those from https://oeis.org/A307717, https://oeis.org/A226782, or https://oeis.org/A226784 by computing power series representations of their correctly guessed generating functions. We designed the algorithm for the more general context of univariate P -recursive sequences. Our implementations are available at http://www.mathematik.uni-kassel.de/~bteguia/FPS_webpage/FPS.htm
Research on traditional Mongolian sentiment analysis combined with prior knowledge

In order to solve some problems of traditional machine learning algorithms in Mongolian sentiment analysis tasks, such as low accuracy, few sentiment corpus, and poor training effect, a Traditional Mongolian sentiment classification algorithm integrates prior knowledge is proposed. First and foremost, 1.3 million unlabeled Mongolian corpora are pre-trained and preliminarily segmented to obtain basic Mongolian vocabulary information. Secondly, the regularization method is used to segment the corpus. Next, a Mongolian text sentiment dictionary is created, which is used as prior knowledge. At the same time, an attention mechanism is integrated into the model to obtain emotional features in a dynamic form. In the last step, based on the Mongolian sentiment data set, the model is further trained and slightly modified to obtain the final traditional Mongolian sentiment analysis model. From the experimental results, the proposed model, compared with the classical sentiment classification algorithms such as Fasttext, BiLSTM, and CNN, has stronger feature extraction ability, sentiment classification performance, and faster convergence speed.
A Study on the Firm Life Cycle Forecasting Model Using Machine Learning Based on News Articles

This paper proposes a method of predicting a firm life cycle by using news data from unstructured text data, which is an information source that has not been used in the past. This paper also proposes a method of selecting keyword candidates that can represent a firm life cycle of a company and quantify the life cycle of the company by using four machine learning techniques.
Strengthening College Students’ Education Under the Condition of Education Informatization Research on Excellent Traditional Culture Education

Chinese excellent traditional culture is profound and far-reaching, and the spiritual core contained in it provides valuable and rich spiritual and cultural resources for us to cultivate and educate college students to become newcomers of the times. Under the trend of contemporary education informatization, we should continue to improve the construction of excellent traditional culture curriculum system, further improve the teaching mode and method, and strive to create a good cultural atmosphere, so as to give full play to the role of excellent traditional culture in the process of training and education of college students.
Design of Multi-functional English Learning App Based on Artificial Intelligence

With the development of electronic information technology and the popularity of intelligent mobile devices, more and more mobile terminal Apps have been developed including many English learning Apps. This paper aims to design a multi-functional English learning App based on artificial intelligence. The App can use the database comparison technology to generate targeted knowledge points annotation, and has many other functions, such as intelligent selection according to the thesaurus, independent dubbing of videos, remembering words by watching news, and reminding users to review according to the forgetting curve. In this way, users' needs can be met to the maximum extent and English learning interest can be improved. The advantage of this App is that, with the help of artificial intelligence technology, it can obtain users' usage habits and recommend appropriate learning materials to users with different English levels according to their individual needs. It strives to make the software design more humanized, intelligent and easy to use in order to provide effective help for the user's English learning.
MLSAN: Mixed-Lattice Self-Attention Network for Chinese Named Entity Recognition

Named entity recognition (NER) is an essential subtask in natural language processing field. Recent studies have demonstrated that character-word lattice models are efficient for Chinese NER, which can leverage useful word boundary information to enhance the representation of characters. However, previous models only consider the integration of local matched word features and neglect the semantic interactions with long-range matched words. Moreover, prior methods solely achieve superficial fusion in the character-word feature space with simple methods, such as feature concatenation, but fail to implement fine-grained semantic fusion. In this paper, we propose a mixed-lattice self-attention network (MLSAN) to integrate richer word boundary information, which can explicitly capture the fine-grained correlations across characters and long-range matched words and achieve the integration of global word features. In addition, we design an end-to-end method for incorporating lexical information at the bottom layer of BERT. Compared with existing methods, our model achieves a deeper lexical knowledge fusion, which makes MLSAN perform well on cases of a small number of samples. Experimental results on four Chinese NER datasets show that our model obtains competitive performance.
Design of a Sensor Network for Drinking Water Control in the Maria of Merced Educational Unit

The excessive consumption of drinking water in the María of the Merced Educational Unit brings with it the need to establish control over it, being a private entity that is located in the Portoviejo canton of the province of Manabí in Ecuador; it is here where it is proposed for this research to design a wireless sensor network over the educational institution. For this purpose, the ZigBee communications protocol was used since this technology allows creating the scope of the network, delimited by approximately 140 square meters, in addition to providing adequate management of battery performance and bandwidth. Several tree, star, and mesh topology scenarios were defined and evaluated by simulation using effective transfer rate, latency, and packet loss parameters. As a result, the tree topology is the most suitable for use in this context. To validate the results, a quasi-experiment was defined in which significant differences between the three topologies were analyzed using the Kruskal Wallis test, allowing the results to be corroborated by statistical tests.
Enhancing Seq2seq Math Word Problem Solver with Entity Information and Math Knowledge

Devising an automatic Math Word Problem (MWP) solver has emerged as an important task in recent years. Various applications such as online education and intelligent assistants are expecting better MWP solvers to process complex user queries that involve numerical reasoning. Current seq2seq MWP solvers encounter two critical challenges: ordinal indices without semantics and insufficient training data. In this work, we propose Entity Random Indexing to equip indices with semantics and design diverse representations of math expressions to augment training data. Experimental results show that our approach effectively enhances the seq2seq MWP solver, which outperforms strong baselines.
Blockchain-Enabled Secure Big Data Analytics for Internet of Things Smart Applications

Smart devices in an Internet of Things (IoT) generate a massive amount of big data through sensors. The data is used to build intelligent applications through machine learning (ML). To build these applications, the data is collected from devices into data centers for training ML models. Usually, the training of models is performed on central server, but this approach requires the transfer of data from devices to central server. This centralized training approach is not efficient because the users are much less likely to share data to the centralized data centers due to privacy issues and bandwidth limitations. To mitigate these issues, we propose an efficient hybrid secure federated learning approach with the blockchain to securely train the model locally on devices and then to store the model and its parameters into the blockchain for traceability and immutability. A detailed security and performance analysis is presented to show the efficacy of the proposed approach in terms of security, resilience against many security attacks, and cost effectiveness in computation and communication as compared to other existing competing schemes.
Automatic Detection of Irritable Bowel Syndrome for 3D Images Using Supervoxel and Graph Cut Algorithm

Medical image processing is a complex exercise and involves a number of stages to identify the disease in the arena of medical imaging. Irritable bowel syndrome is an acute disorder that causes intense abdominal pain and leads to changes in the bowel system. It gives rise to various indications like bleeding, bloating, celiac disease, gastric cancer, ulcer, etc. The system proposed here seeks to segment and classify each symptom of the irritable bowel syndrome individually with the aid of supervoxel segmentation algorithm. Features are extracted depending on the color, shape, and texture of the object. The extracted features are fed into the multi-support vector machine to identify the specific region in the medical image. The experiment provides the result of a test set 100 images stored in the data set which improves accuracy that refines the final output.
Comparison and Application of Two Face Detection Algorithms

This chapter compares two face detection algorithms Viola-Jones and MTCNN that have certain similarities in the structure of the algorithm. They are both creative and significant objective detection algorithms though they are based on different frameworks. To have a more intuitive view of the merits and demerits of two face detectors, we test two detectors on a challenging dataset including 25 images divided into 5 categories and compare the results in terms of detecting accuracy, time consumed on the detection, and effects of nonface features on detection results. Besides, for the results of MTCNN, it is essential to observe the positions of the five feature points which are key indicators of accuracy. By comparing the results, it is clear that the detection rate of MTCNN is generally higher than that of Viola-Jones, but the detection speed of Viola-Jones is faster. Furthermore, Viola-Jones has a good overall performance in positive face detection and is better suited for fast detection in frontal view, whereas MTCNN is better suited for industrial-grade scenes requiring high accuracy.
Evaluation Algorithm of Staff Ability Value in Personnel Management System Based on .Net

In order to better evaluate the ability value of employees, this paper will combine .Net personnel management system in the evaluation algorithm research, mainly discussed .Net personnel management system characteristics and concepts, after the development of the system design, synchronous introduction of the algorithm, the final system function test, verify whether the system can accurately assess the value of staff ability. Through the test, the accuracy of the calculation results of the evaluation of the ability value of the staff is up to standard, so the system is effective. At the same time, it shows that the design method and algorithm of the system in this paper is correct and has practical value.
Applying the hierarchy analysis method to evaluate learning outcomes

The article proposes to use the mathematical tools of the hierarchy analysis method in the educational process to assess the level of student achievement results. An example of calculating knowledge assessment indicators using the method of hierarchies in the educational process of a university in studying a discipline using the traditional approach and individual educational trajectories is considered. The use of the hierarchy analysis method is possible in any educational organization when studying courses and disciplines in a traditional, hybrid or online format. The method will be of interest to teachers who use in the educational process a point-rating system for assessing learning outcomes with a variety of indicators of varying significance. A systematic analysis of the individual achievements of students, implemented using the hierarchy analysis method, allows the teacher to determine the final grade for the course as objectively as possible.
Neural Networks

Deep learning neural networks (NN) are playing an increasingly important role in modern machine learning. Deservingly so, as such models provide researchers with versatile methods and powerful tools for supervised learning. Sufficiently extended deep networks can represent quite complex functions. Although not all powerful, in that they cannot replicate sophisticated human thinking, deep networks perform extremely well in most tasks that involve mapping an input vector to an output vector.
Using Cohesion-Based and Sentiment-Based Attributes to Classify Spoilers in Movie Reviews

Spoiler reviews have different narrative patterns from non-spoiler reviews. Their narrative is more precise about what happened in the movies, while that of non-spoiler reviews is more obscure due to the omission of specific details. Our research extracted 108 cohesion-based and 6 sentiment-based attributes from movie reviews, which captured these patterns. The classification was done using Naive Bayes and a support vector machine (SVM) with a linear kernel. SVM achieved the best performance of 78% accuracy and 0.78 F -measure of class spoiler. Most contributing attributes were also determined from the weight vector given by the SVM. They supported our initial observation about the differences in narrative patterns between spoilers and non-spoilers.
FP$$^2$$-MIA: A Membership Inference Attack Free of Posterior Probability in Machine Unlearning

Generally speaking, machine learning is to train an ML model (original model) on a dataset to perform a certain function. But sometimes, in order to protect the data privacy of a specified user, machine unlearning requires the original model owner to delete the specified user’s data in its training dataset and retrain a new model (unlearned model). However, the research of CCS’21 shows that the adversary can judge whether a data sample is deleted by comparing the prediction vectors of the original and unlearned models, thus being attacked by membership inference. To mitigate this privacy leak, CCS’21 proposes that models that only output predicted labels (i.e., , cannot obtain model posterior probabilities) can effectively defend against existing attacks. However, our research shows that even machine unlearning models that only output labels have certain privacy risks. This paper proposes an inference attack that does not rely on posterior probability against machine unlearning, named FP $$^2$$ -MIA. Specifically, the adversary queries the original and unlearned models for candidate data samples respectively, and adds perturbations to them to change the predicted labels of the two models, and then the adversary uses the magnitude of the perturbations to distinguish whether they are deleted data samples. We conduct experiments on four datasets, MNIST, CIFAR10, CIFAR100 and STL10. The results show that member inference can be effectively inferred even when only the predicted labels are output, in which the AUC (Area Under Curve) index on the MNIST dataset is as high as 0.96.
APDP: Attribute-Based Personalized Differential Privacy Data Publishing Scheme for Social Networks

In the Big Data era, the wide usage of mobile devices has led to large amounts of information release and sharing through social networks, where sensitive information of the data owners may be leaked. Traditional approaches that provide the identical privacy protection levels for all users result in poor quality of service, thus the concept of personalized privacy has been proposed in recent years. However, existing methods that add different noises to each user will require both high real-time performance and resource consumption. This paper presents a fine-grained personalized differential privacy data publishing scheme (APDP) for social networks. Specifically, we design a new mechanism that defines the privacy protection levels of different users based on their attribute values. In particular, we exploit the TOPSIS method to map the attribute values to the amount of noise required to add. Furthermore, to prevent illegal download of data, the access control is integrated with differential privacy. Compared with traditional attribute-based encryption data publishing schemes, our scheme can get rid of the expensive computation overhead. Theoretical analyses and simulations show that APDP can realize efficient personalized differential privacy data publishing with reasonable data utility.