Reviewerly/exHarmony huggingface dataset:
Diagnosis of Diseases: Classification Rule Discovery from Medical Data using Genetic Algorithm with Suppressor Mutation

Data mining is a powerful method to extract knowledge from data. Data mining area focused an attractive research challenges on Knowledge Discovery in Databases (KDD), with the aim to discover interesting and useful data from various transactional databases. Association Rule Mining is one of the most well-known techniques for extracting relations among attributes from larger databases which is given in the form of rules to the user. Data mining techniques in medical domain is inevitable in diagnosing various diseases which leads to reduce time and performance, by taking number of tests required for the patients. This paper presents a rule extraction for diabetes disease using genetic algorithm, with a newly proposed genetic operator named suppressor mutation (GA-SM), to suppress the activity of genes which are over expressive in nature i.e. to suppress the useless attributes from the rules. By using this new operator in association rule mining techniques, it generates a smaller number of rules for diagnosing diabetes. Genetic algorithm shows a wonderful performance with suppressor mutation when compared to CN2, J48, BF tree with respect to performance factors.
Professionalism and professional education in the context of sustainable development

The sustainable development serves as the resulting index of new economic, social and political development in society with multiple uncertain parameters due to the increased process dynamics. It foresees the creation of personnel training system, the personnel able to develop proactively and fit the times. Consequently, it sets other tasks to the professional education system. The analysis conducted by the authors is directed to studying the term “professionalism” on the example of the social sphere, the dynamics of key professional functions and possibilities of social sphere specialists’ professional conversion. Respectively, primary trends in the social education development and the compliance with the labour market needs in social sphere, the impact of professional activity of social sphere specialists towards sustainable development in our country were analysed. The evaluation of study results was carried out during the development of professional and education standards, their professional and public hearings, and the development of recommendation on application of network and distance technologies in the social education.
A Hybrid Method of Facial Expressions Recognition in the Pictures

The following article gives an insight into a new hybrid method of facial expression recognition and its usage on an open-source Google dataset. The paper also explains chosen methods of picture analysis that are generally known and available, which correctly recognize the specific facial expression shown on the face of a photographed person. The conducted experiments proved the effectiveness of the developed approach in comparison with the naive Bayes Classifier, Support Vector Classifier, and Convolutional Neural Network in terms of F1 measure.
Application of the Cycles Merging Algorithm to the Shortest Common Superstring Problem

This paper describes the application of the cycles merging algorithm for data compression using an approximate solution to the Shortest Common Superstring (SCS) problem, which is reduced to solving the asymmetric traveling salesman problem to the maximum (MaxTSP). SCS is as follows: given a collection of strings $s_{1},\ldots, s_{m}$, find the shortest string s such that each s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> appears as a substring (a consecutive block) of s. The algorithm is tested for data compression on randomly generated data with the specified parameters, the results are compared with the results of data compression by greedy and 4-approximation algorithms. The efficiency of the CMA algorithm for the maximum problem is investigated.
A Type-Specific Attention Model For Fine Grained Entity Type Classification

Sahay, Atul Arya, Kavi Gholkar, Smita Mukherjee, ImonDifferent methods exist for Named Entity Type Classification (NETC), ranging from primitive rule-based learning to Deep Learning methods. This paper discusses notable parts of significant methods that exist in the domain. In the process of researching these methods, we observed certain drawbacks in the current Fine Grained Entity Type Classification (FETC) methods. Ignoring local contextual linguistic information and ignoring type membership information of these contextual entities are two main drawbacks we have focused upon. We propose an approach that overcomes both these drawbacks by employing a Bi-Directional Short Term Memory (Bi-LSTM) network with a layer of “type-specific” attention. Our approach achieved $$\sim $$ 2% increased improvement on F1 scores over and above the state-of-the-art work on the publicly available dataset FIGER (GOLD) with considerably lesser training time.
Time-Series Big Data Stream Evaluation

Big data processing is a challenging job. Extensive time-series data need a method of preparation, management, and feature calculation for each data arrival. FIMT-DD is an algorithm for processing predictive regression for big data. The splitting criteria in the standard FIMT-DD algorithm use a Hoeffding Bound. We propose to change the splitting criteria to Chernoff bound. The experimental results and the performance comparisons that we did have better results than the standard method. We use three real-world datasets. The improvement that we propose can produce a 2.3% accuracy improvement for traffic demand data.
FTL: A Universal Framework for Training Low-Bit DNNs via Feature Transfer

Low-bit Deep Neural Networks (low-bit DNNs) have recently received significant attention for their high efficiency. However, low-bit DNNs are often difficult to optimize due to the saddle points in loss surfaces. Here we introduce a novel feature-based knowledge transfer framework, which utilizes a 32-bit DNN to guide the training of a low-bit DNN via feature maps. It is challenge because feature maps from two branches lie in continuous and discrete space respectively, and such mismatch has not been handled properly by existing feature transfer frameworks. In this paper, we propose to directly transfer information-rich continuous-space feature to the low-bit branch. To alleviate the negative impacts brought by the feature quantizer during the transfer process, we make two branches interact via centered cosine distance rather than the widely-used p-norms. Extensive experiments are conducted on Cifar10/100 and ImageNet. Compared with low-bit models trained directly, the proposed framework brings 0.5% to 3.4% accuracy gains to three different quantization schemes. Besides, the proposed framework can also be combined with other techniques, e.g. logits transfer, for further enhacement.
Self-organising Neural Network Hierarchy

Mammalian brains exhibit functional self-organisation between different neocortical regions to form virtual hierarchies from a physical 2D sheet. We propose a biologically-inspired self-organizing neural network architecture emulating the same. The network is composed of autoencoder units and driven by a meta-learning rule based on maximizing the Shannon entropy of latent representations of the input, which optimizes the receptive field placement of each unit within a feature map. Unlike Neural Architecture Search, here both the network parameters and the architecture are learned simultaneously. In a case study on image datasets, we observe that the meta-learning rule causes a functional hierarchy to form, and leads to learning progressively better topological configurations and higher classification performance overall, starting from randomly initialized architectures. In particular, our approach yields competitive performance in terms of classification accuracy compared to optimal handcrafted architecture(s) with desirable topological features for this network type, on both MNIST and CIFAR-10 datasets, even though it is not as significant for the latter.
Domain Intelligent Q&amp;A user intention recognition based on keyword separation

In view of the disadvantages of current intelligent Q&A user intention recognition technology, which can't make good use of the detailed features in user questions, this paper proposes a layered method to identify user intention. This method first identifies the keywords in the user problem as the user's first level intention, then distinguishes the sentence pattern of the user problem according to the number of keywords, and determines whether to identify the second level intention according to the sentence pattern of the problem. Then, the key word features are fused to identify the user's problem type, and the result of problem type recognition is regarded as the second layer of user's intention. Finally, the user's two layers of intentions are integrated and regarded as the user's overall intentions, and retrieved in the Knowledge Graph. Experiments show that the F1 value of user problem type recognition is increased by 6% after keyword features are added. The method of keyword separation is used to identify the user's intention in question and answer, which separates the two layers of user's intention. For the problem sentence pattern that is not necessary to recognize the second layer of user's intention, the judgment of user's intention is no longer performed, which reduces the complexity of user's intention recognition, and opens up space for the case that keywords need to be processed. In addition, keyword features are added to the second layer of user intention recognition model, which makes better use of the detailed features in user question statements and improves the effect of user intention recognition.
A Global to Local Double Embedding Method for Multi-person Pose Estimation

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method.
CSL+

Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources and/or fusing conflicting opinions from different entities. To explicitly deal with uncertainty, a belief model called Subjective Logic (SL), as a variant of Dumpster-Shafer Theory, has been proposed to represent subjective opinions and to merge multiple opinions by offering a rich volume of fusing operators, which have been used to solve many opinion inference problems in trust networks. However, the operators of SL are known to be lack of scalability in inferring unknown opinions from large network data as a result of the sequential procedures of merging multiple opinions. In addition, SL does not consider deriving opinions in the presence of conflicting evidence. In this work, we propose a hybrid inference method that combines SL and Probabilistic Soft Logic (PSL), namely, Collective Subjective Plus, CSL + , which is resistible to highly conflicting evidence or a lack of evidence. PSL can reason a belief in a collective manner to deal with large-scale network data, allowing high scalability based on relationships between opinions. However, PSL does not consider an uncertainty dimension in a subjective opinion. To take benefits from both SL and PSL, we proposed a hybrid approach called CSL + for achieving high scalability and high prediction accuracy for unknown opinions with uncertainty derived from a lack of evidence and/or conflicting evidence. Through the extensive experiments on four semi-synthetic and two real-world datasets, we showed that the CSL + outperforms the state-of-the-art belief model (i.e., SL), probabilistic inference models (i.e., PSL, CSL), and deep learning model (i.e., GCN-VAE-opinion) in terms of prediction accuracy, computational complexity, and real running time.
A band Mura detection method based on a new generative adversarial network

Defined as a visual inhomogeneity, Mura can cause seriously unpleasant feelings and that’s why it needs to be inspected. Band Mura, which has a large area, is particularly difficult to be detected because of its irregular shape and size as well as its low contrast. So we propose a UADD-GAN model to detect band Mura in this work. Consisting of a proposed UADD generator and a discriminator, the model is trained with some normal samples, after which the generator is able to simulate the distribution of normal samples. During training, the generator takes normal images as inputs and output their reconstructions, while the discriminator receives images and determines whether they’re true or fake, defiantly helping the generator to perform reconstructions better. The symmetric structure and operation of skipadding make it easy for the UADD generator to reconstruct the normal samples well. On the contrary, the generator performs worse in the reconstruction of Mura samples so that we can distinguish them from the normal ones. In addition, we use multiple feature layers of the discriminator for supervision instead of using only the classification layer, helping the generator to reconstruct normal samples better. We’ve conducted experiments of MURA data sets with different proportion and our research indicates that the proposed model surpass other state of the art methods.
Research on the Teaching Reform of Managing the Software Process Course

In view of the problems existing in the teaching of software process management, combined with the standard requirements of engineering education certification and the cases of the implementation of relevant courses in foreign high-level universities, this paper puts forward the reform measures for the practical teaching of this course. It is suggested that the practical teaching of the course should adopt the way of integrating the cases with appropriate difficulty and scale with the course projects, forming the students into the corresponding project team to complete the practical teaching of the course in the scene of the software development company, reforming the corresponding course assessment and evaluation mode, so that the students can complement each other and really learn something. We should realize the teaching goal of software engineering specialty in the new era, and train qualified software professionals.
Near Real Time Twitter Sentiment Analysis and Visualization

Abstract Twitter can be considered as a large scale network. People’s opinions matter a lot to analyze how knowledge spreads impact lives. In this project, we took advantage of the Apache Spark Streaming fast and memory computing platform to retrieve live tweets and perform sentiment analysis. The primary purpose is to provide a tool to evaluate the score of sentiments in streams. This paper reports on the nature of an analysis of emotions, collecting vast numbers of tweets. Results identify the view of users through tweets into positive, neutral and negative about coronavirus. This project on Spark Streaming to analyze tweets, hashtags or specific keyword/keywords such as (corona) from live twitter data streams. Data is collected from input sources like Twitter and processed downstream using Spark Streaming. Then, how sentiment scores can be generated for tweets and build visualization dashboards on the data using Elasticsearch and Kibana.
Deep Learning-based Object Understanding for Robotic Manipulation

Manipulation of objects by a robot arm requires an understanding of the various properties of the object. The robot needs a lot of information for object manipulation, there are few algorithms to estimate such information simultaneously. In this study, we propose an object understanding network (OUNet) based on deep learning that simultaneously estimates three key properties for robot object manipulation: object state, contact position for object manipulation, and manipulation type. The object state means whether an openable object is open or closed. The contact position and manipulation type for manipulating objects means where and what the robot should do to change the object state. Usingthis information, it is expected that the robot will be able to select the appropriate manipulation for the current situation of the given object. Experiments were conducted to verify the performance of the OUNet, and it was shown that three key properties can be successfully detected.
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network architectures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process. In architecture searching stage, we mea-sure the similarity of the pruning mask to get high-quality network architecture with low energy and time consumption. After searching stage, our proposed method randomly sample the compact architectures with different pruning rates and input resolution to achieve joint optimization. Ultimately, we can obtain a cohort of compact networks adaptive to various resolution to meet dynamic FLOPs constraints on different edge devices with only once training. The experiments based on image classification and object detection show that OFARPruning has a higher accuracy than the once-for-all compression methods such as US-Net and MutualNet (1-2% better with less FLOPs), and achieve competitive performance as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2 under 170 MFLOPs) with much higher efficiency.
Computing Expected Differential Probability of (Truncated) Differentials and Expected Linear Potential of (Multidimensional) Linear Hulls in SPN Block Ciphers

In this paper we introduce new algorithms that, based only on the independent round keys assumption, allow to practically compute the exact expected differential probability of (truncated) differentials and the expected linear potential of (multidimensional) linear hulls. That is, we can compute the exact sum of the probability or the potential of all characteristics that follow a given activity pattern. We apply our algorithms to various recent SPN ciphers and discuss the results.
Improving discourse representations with node hierarchy attention

Long text representation for natural language processing tasks has capture researchers’ attention recently. Beyond the sentence, finding a good representation for the text turns to the bag of the words that losses sequence order. Indeed, the text does not pattern in a haphazard way; rather, in a coherent document there exist systematic connections between sentences. Rhetorical structure theory models this connection in a tree structure format. This tree models text span and their relation. The importance of each text span is distinguished by their hierarchy type in the tree named nucleus and satellite. In this paper, we try to enrich text representation by taking into account the contribution of each phrase in the text based on its hierarchy type. We employ a deep recursive neural network as the attention mechanism to improve text representation. Our hypothesis is evaluated in a sentiment analysis framework. In addition, basic recursive neural network and predefined weighting attention consider as benchmarks. Results show that reweighting span vectors via a deeper layer of recursive neural network outperforms predefined scalar and no attention methods.
An Architecture for Game to Game Data Transfer Using Blockchain

Blockchain has been used in several industries, especially in the financial area. In the game industry, blockchain has not been utilized much even with a large number of users who deserve to own their game data. There were attempts to allow players to own their in-game objects or data. But they were mostly crowdfunding platforms which were not yet in operation. In addition, users were required to use their ecosystem. In this paper, we propose a generic game architecture, using blockchain, that allows players to own their “spent time” in a game. The value of this spent time can be passed on to new games as ERC20 token on the Ethereum network and redeemed in such games. Our architecture can be used with any public blockchain. It can benefit many roles such as player, blockchain node, and game developer. Our experiment shows that the proposed architecture can be accepted by players, but other factors such as the type of gameplay and how the spent time is exchanged in a game can greatly affect the player acceptance.
Channel-Wise Attention and Channel Combination for Knowledge Distillation

Knowledge distillation is a strategy to build machine learning models efficiently by making use of knowledge embedded in a pretrained model. Teacher-student framework is a well-known one to use knowledge distillation, where a teacher network usually contains knowledge for a specific task and a student network is constructed in a simpler architecture inheriting the knowledge of the teacher network. This paper proposes a new approach that uses an attention mechanism to extract knowledge from a teacher network. The attention function plays the role of determining which channels of feature maps in the teacher network to be used for training the student network so that the student network can only learn useful features. This approach allows a new model to learn useful features considering the model complexity.
Query by Strings and Return Ranking Word Regions with Only One Look

Word spotting helps people like archaeologists, historian and internet censors to retrieve regions of interest from document images according to the queries defined by them. However, words in handwritten historical document images are generally densely distributed and have many overlapping strokes, which make it challenging to apply word spotting in such scenarios. Recently, deep learning based methods have achieved significant performance improvement, which usually adopt two-stage object detectors to produce word segmentation results and then embed cropped word regions into a word embedding space. Different from these multi-stage methods, this paper presents an effective end-to-end trainable method for segmentation-free query-by-string word spotting. To the best of our knowledge, this is the first work that uses a single network to simultaneously predict word bounding box and word embedding in only one stage by adopting feature sharing and multi-task learning strategy. Experiments on several benchmarks demonstrate that the proposed method surpasses the previous state-of-the-art segmentation-free methods. (The code is available at https://github.com/zhaopeng0103/WordRetrievalNet.)