Reviewerly/exHarmony huggingface dataset:
Towards Better Caption Supervision for Object Detection

As training high-performance object detectors requires expensive bounding box annotations, recent methods resort to free-available image captions. However, detectors trained on caption supervision perform poorly because captions are usually noisy and cannot provide precise location information. To tackle this issue, we present a visual analysis method, which tightly integrates caption supervision with object detection to mutually enhance each other. In particular, object labels are first extracted from captions, which are utilized to train the detectors. Then, the objects detected from images are fed into caption supervision for further improvement. To effectively loop users into the object detection process, a node-link-based set visualization supported by a multi-type relational co-clustering algorithm is developed to explain the relationships between the extracted labels and the images with detected objects. The co-clustering algorithm clusters labels and images simultaneously by utilizing both their representations and their relationships. Quantitative evaluations and a case study are conducted to demonstrate the efficiency and effectiveness of the developed method in improving the performance of object detectors.
Enriching BERT With Knowledge Graph Embedding For Industry Classification

Industry classification for startup companies is meaningful not only to navigate investment strategies but also to find potential competitors. It is essentially a challenging domain-specific text classification task. Due to the lack of such dataset, in this paper, we first construct a dataset for industry classification based on the companies listed on the Chinese National Equities Exchange and Quotations (NEEQ), which consists of 17, 604 annual business reports and their corresponding industry labels. Second, we introduce a novel Knowledge Graph Enriched BERT model (KGEB), which can understand a domain-specific text by enhancing the word representation with external knowledge and can take full use of the local knowledge graph without pre-training. Experimental results show the promising performance of the proposed model and demonstrate its effectiveness for tackling the domain-specific classification task.
HAMLET: Hierarchical Attention-based Model with muLti-task sElf-Training for user profiling

User profiling is playing an increasingly important role in real-world applications. Previous works have shown that integrating user information from multiple social networks helps to significantly improve the performance of user profiling. However, these studies either ignore the different contributions of various features in different profiling tasks or need to train one model for each task. What’s more, the assumption of the strong relatedness between user profiling tasks limits their application. These phenomena make inferring comprehensive user attributes still an open problem. In this paper, we propose a novel method, called Hierarchical Attention-based Model with sparse-sharing-based muLti-task sElf-Training algorithm (HAMLET), for comprehensive user profiling. More specifically, we first employ a hierarchical attention-based network as our base network to represent users. It assigns various features from different social networks with different weights for different users during the fusing procedure. Then, we propose a multi-task self-training algorithm that takes advantage of both task correlations and self-training to obtain better performance. We conduct extensive experiments on two real-world datasets and verify the superiority of HAMLET for user profiling.
Time and Position Aware Graph Neural Networks for Session-based Recommendation

Session-based recommendation system is an important part of many e-commerce sites. Its purpose is to recommend according to the interaction behavior of anonymous users in a short time. The latest research is to model the session sequence as a graph and then use the graph neural network to learn the embedding of the item. However, these methods treat the session as a simple sequence and ignore the time interval between user’s adjacent interactions. In order to solve this problem, we propose a Time and Position Aware Graph Neural Networks model for the session-based recommendation systems, which can not only learn the embedding of items, but also capture users' interest by using the time interval and sequence information when users browse items. We have conducted sufficient experiments on two e-business datasets, and the experimental results show that our model is superior to baselines.
Cognitive Biases as Clues to Skill Acquisition in Manufacturing Industry

Although robotization and mechanization are recently increasing in manufacturing plants, small- and medium sized companies are burdened with the costs of introduction and have no choice but to rely on human labor when improving work efficiency. Here, in an effort to improve efficiency in manufacturing industrial parts, we analyzed the visualized gaze data of both an apprentice and a skilled worker using eye-tracking hardware/software. We found that self-recognition of situations where cognitive biases, which are Ease of Recall and Confirmation Trap, was in play would be associated with skill acquisition and attainment of the next learning level for work efficiency.
An Automatic Modulation Classification Method for Multi-signal Scenario Based on ULA

Traditional digital modulation recognition methods exist following defects including performance deterioration at low SNR and lack of recognition capability when facing multi-signal scenario. Array antenna can separate signals individually by spatial filtering, improve SNR of received signal, and will contribute to multi-signal digital modulation recognition using traditional methods designed for single source. Taking advantage of uniform linear array, a multi-signal modulation recognition method was proposed which uses high-order cumulant feature, instantaneous statistical amplitude feature and high-order cumulants of signal after frequency conversion, and finally classical decision-tree classifier is used for digital modulation classification. Simulation results showed that the method proposed in this paper can recognize multi-signal digital modulation efficiently with satisfactory recognition rate at low SNR, and meanwhile influence factors including DOA estimation error and multi-signal array manifold were studied and analyzed.
AI Based Monitoring System for Social Engineering

Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.
AI in (and for) Games

This chapter outlines the relation between artificial intelligence (AI)/machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond to player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.
The epistemological foundations of data science: a critical analysis

The modern abundance and prominence of data has led to the development of “data science” asa new field of enquiry, along with a body of epistemological reflections upon its foundations,methods, and consequences. This article provides a systematic analysis and critical review ofsignificant open problems and debates in the epistemology of data science. We propose a partitionof the epistemology of data science into the following five domains: (i) the constitution of datascience; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data sciencegenerates; (iv) the nature and epistemological significance of “black box” problems; and (v) therelationship between data science and the philosophy of science more generally.
DISC Personality Classification using Twitter: Usability Testing

The employee recruitment process is one of the essential steps needed in creating a solid team, so we need a decision support system to assist Human Resources (HR) in conducting new recruitment, namely the Applicant Profiling Analysis. This application can find out a person's personality to support employee recruitment standards. The model used to determine employees' personality is the DISC (Dominance, Influence, Steadiness, Compliance). This research aims to analyze and measure the usability of the personality classification system in classifying personality using the DISC model on tweets on social media Twitter of the applicant. DISC personality classification system has a 100% success rate, where none of the users fail to operate the application. Usability Testing in this resereach using User Experience Questionare (UEQ). The level of user experience using obtained the good of each scale tested with aspect Clarity, Efficiency, and Accuracy.
Evaluating a Plan Recognition Agent for the Game Pandemic with Human Players

Cooperation between AI agents and humans is of ever greater importance. In this paper we present an AI agent for the game Pandemic that was specifically designed to play cooperatively with a human player. Our agent utilizes planning to determine which actions to perform, and plan recognition to determine the current goal of its cooperator in order to assist them. We also present an experiment we performed with human participants, and how our agent performs at a level that is comparable to other AI agents playing with themselves, when playing with a human player, as well as the impact of plan recognition on how the participants perceive the AI agent.
Online inference with multi-modal likelihood functions

Let (Yt)t≥1 be a sequence of i.i.d. observations and {fθ,θ∈Rd} be a parametric model. We introduce a new online algorithm for computing a sequence (θˆt)t≥1, which is shown to converge almost surely to argmaxθ∈RdE[logfθ(Y1)] at rate O(log(t)(1+ε)/2t−1/2), with ε>0 a user specified parameter. This convergence result is obtained under standard conditions on the statistical model and, most notably, we allow the mapping θ↦E[logfθ(Y1)] to be multi-modal. However, the computational cost to process each observation grows exponentially with the dimension of θ, which makes the proposed approach applicable to low or moderate dimensional problems only. We also derive a version of the estimator θˆt, which is well suited to student-t linear regression models that are popular tools for robust linear regression. As shown by experiments on simulated and real data, the corresponding estimator of the regression coefficients is, as expected, robust to the presence of outliers and thus, as a by-product, we obtain a new adaptive and robust online estimation procedure for linear regression models.
Face Image Generation with Features of Specific Group from Small Dataset

The research on face image generation has gained widespread attention in the field of image generation. However, large datasets are required to train a generative model to produce images of high quality and resolution. It is especially difficult to collect multiple face images of a specific group. It may also be difficult to establish an efficient model by training with a small dataset.This paper presents a novel method for the generation of face images with the features of a specific group. The face images of a dataset are embedded into a latent space as sets of latent variables by Image2StyleGAN and are expressed as a distribution of the sets of latent variables in the latent space of a pre-trained StyleGAN2. Principal Component Analysis (PCA) is used to extract the features of the distribution. The generation of images for different groups does not require re-training of the model, as a pre-trained model is used instead. Furthermore, the proposed method can generate high-quality images with the features of the group from a dataset with only about 100 face images. However, the quality and the variety of the generated images can vary depending on a Cumulative Contribution Rate (CCR) of the PCA. Therefore, this study also proposes a metric called the Fréchet Inception Distance in Principal Component Space (FID-PCS), which can evaluate the generated images even with a small dataset. The FID-PCS can be used to determine the CCR that generates images with a good balance between the quality and the variety. The face images of three groups were collected as datasets to evaluate the validity of the proposed method, which include male idols, female idols, and male mixed martial artists. It was observed that images with the features of the group are generated by the face distributions extracted by the PCA, and the images with high quality and wide variety are generated by determining the appropriate CCR by the FID-PCS.
Tweaking NooJ’s Resources to Export Morpheme-Level or Intra-word Annotations

NooJ’s [, ] export function allows its users to convert NooJ output from a binary file to a text file format. However, at present, the NooJ export function does not fully export intra-word units. My solution to this problem is demonstrated using SANTI-morf [], a new morphological annotation system for Indonesian, written as a package in NooJ’s Indonesian language module. While the solution is dedicated to Indonesian morphology, I argue that the method I propose can be replicated by other NooJ users facing the same challenge. A new syntactic grammar whose rules capture morphotactic combinations that form Indonesian polymorphemic words is devised. Dictionaries and morphological grammars are modified to allow all morphemes and their associated analytical attributes to be automatically transferred using the new syntactic grammar as single units, like monomorphemic words. Some symbols that are special to NooJ must be replaced by non-standard symbols as these special symbols are not acceptable in syntactic annotations. This experimentation successfully exports more than 99% of word tokens from the test-bed corpus into full morpheme-level annotations. While successful, the concatenation of these morphemes as single units and the use of non-standard symbols confuse morpheme boundaries and annotations, causing the readability of the output to be low. A small program is then written to improve the readability of the output, which can easily be adapted to users’ anticipated needs.
Hybrid Information Flow Control for Low-Level Code

Failure to ensure data confidentiality can have a significant financial and reputational impact on companies. To aggravate the issue, frequently used methods like testing are insufficient when proving data confidentiality in software systems. Existing information flow based approaches require heavy implementation and specification efforts or lack the expressiveness programmers desire. To tackle the issues, we propose a novel hybrid system for information flow control in low-level languages. By combining an information flow monitor with a type system that instruments programs with runtime security checks, we support value-dependent security types in a low-level setting. We formalise our type system and monitor using a TAL-like calculus and prove that they guarantee termination-insensitive non-interference. We present the first hybrid type system for information flow control with support for value-dependent types. We also introduce the first value-dependent hybrid mechanism for a low-level intermediate representation.
Orchid: Building Dynamic Test Oracles with Training Bias for Improving Deep Neural Network Models

The accuracy of deep neural network models is always a top priority in developing these models. One problem to affect it is to what extent such a model can resolve training samples conflicting with one another while learning from them. Such a model may easily learn the whole training dataset with high recall, leaving the problem only to appear when evaluating the model, and yet samples in the evaluation should not be used in training the model. This paper proposes Orchid, the first work to alleviate the problem of class pairs with training samples conflicting with one another. Orchid firstly rewinds the learning effect of a deep neural network model under fix on different subsets of the training dataset, producing a set of resultant rewound models. It then constructs a novel kind of reference oracle by dynamic selection and integration of these rewound models, each predicting the same sample in question correctly. It finally adapts the model under fix by retraining it on training samples with the reference oracle. The experiment on MobileNet and ShuffletV2 with the Cifar-100 and Tiny-ImageNet datasets shows that their test accuracies are improved over the baselines by 2.47% and 2.62%, respectively. Orchid also produces noticeable reductions in misclassification between classes of the adapted models on the test datasets.
Design and implementation of process form translation component based on business service catalog

Many enterprises manage their IT operation and maintenance services through the operation and maintenance management platform, i. e. ITSM. With the expansion and improvement of the scope and degree of business informatization, IT operation and maintenance services are facing more and more businesses, and the content in the service catalog is richer and finer. In order to realize the decoupling of operation and maintenance service management from different business areas and improve the scalability of ITSM, it is proposed to build ITSM as the "Middle Platform" solution. The process form translation component based on the business service catalog provides simple configuration to complete the conversion between the business application form of different business platforms and the service form of the operation middle platform, the implementation form of the operation middle platform and the standing books of the business platform through simple configuration, so as to realize the unification and aggregation of data from multiple business sources. In addition, it supports the rapid integration of the operation center with new businesses or new business platforms of the business platform, greatly improving the scalability of the operation middle platform.
Empirical Rule Induction Methods Selection

For data mining in big data, selection of subsets from data and selection of rule induction are very critical issues. This paper proposes a method for selection of rule induction by using hierarchical sampling. First, the method generates training samples and test samples in a two-level hierarchical way.Second, it generates new training sub-samples and test sub-samples from training samples. Third, rule induction methods construct the models from training sub-samples and evaluate the model by new test samples. The second and third process are repeated for a given number of trials and evaluation indices are calculated as mean values of all the trials. Then, fifth, the method compared the results and select the best method. And finally, the selected method is used for construction of the model by using first training samples and the model is evaluated by first test samples. We evaluated this method on seven medical datasets. The results show that this method gives better performance than conventional methods.
Dialectical Analysis of Comparative Pedagogy Based on Multiple Intelligences Evaluation

As a common and mature algorithm, the neural network algorithm has been widely used in many industries throughout the country. The traditional dialectical analysis method for multiple intelligences evaluation in comparative education cannot meet the dialectical needs with different characteristics, the information big data model of multiple intelligences evaluation based on neural network algorithm has been gradually applied to several evaluation systems of comparative education. This paper studies the application of neural network algorithms in the dialectical analysis of comparative education in China and puts forward multiple intelligences evaluation model based on neural network algorithm, which can realize the intelligent evaluation of comparative education according to the characteristics of teaching behavior. At the same time, the idea of random big data acquisition is combined with digital feature analysis based on neural network algorithm and particle swarm optimization algorithm. Finally, the experimental results show that the dialectical analysis model of comparative education based on multiple intelligences evaluation of neural network algorithm can efficiently process the education data with tracking intelligence, which achieves a new breakthrough in the multiple intelligences evaluation of comparative education in China and saves a lot of time for the dialectical analysis process.
STRUCTURE, PARAMETERS AND FUNCTIONS OF SELF-RELATION

Self-attitude is a complex psychological phenomenon that characterizes the position of a person in relation to himself. The characteristics of self-attitude determine how adequately a person will be able to adapt in the sphere of his professional activity and affect his socially significant activity. Self-attitude is a kind of self-regulating mechanism that is present at all stages of behavioral activity, starting from the stage of motive formation and ending with the analysis of the result of activity.
Automatic White Blood Cell Detection Depending on Color Features Based on Red and (A) in the LAB Space

Automatic detection of white blood cells remains an unresolved issue with medical imaging. Researchers from the fields of computer display and medicine participated in the analysis of WBC images. A new algorithm is proposed in this paper to detect white blood cells, and this algorithm is based on the binary conversion of red, and A component in the LAB space after determining some strong values and eliminating unwanted small areas depending on the medium filter. In the results of experimental white blood cell imaging, the proposed algorithm was compared with several other detection algorithms. The quality was determined based on the precise calculation of the comparison to determine the area of white blood cells, and theoretically, through the proposed algorithm, the automated diagnosis of these regions. The diagnostic accuracy of WBC is the measurement from the following accuracy, sensitivity, and specificity as the proposed algorithm achieved a high differential accuracy of 98.5%.