Reviewerly/exHarmony huggingface dataset:
Few-Shot Representation Learning for Cold-Start Users and Items

Existing recommendation algorithms suffer from cold-start issues as it is challenging to learn accurate representations of cold-start users and items. In this paper, we formulate learning the representations of cold-start users and items as a few-shot learning task, and address it by training a representation function to predict the target user (item) embeddings based on limited training instances. Specifically, we propose a novel attention-based encoder serving as the neural function, with which the K training instances of a user (item) are viewed as the interactive context information to be further encoded and aggregated. Experiments show that our proposed method significantly outperforms existing baselines in predicting the representations of the cold-start users and items, and improves several downstream tasks where the embeddings of users and items are used.
Single-image depth estimation by refined segmentation and consistency reconstruction

Recent years have witnessed tremendous success of single-image depth estimation. However, most of the existing approaches merely use scene descriptions of a whole image to retrieve its candidates, which may end up with undesirable depth supports for local regions. In this paper, we propose a segmentation method for single-image depth estimation based on data-driven framework. First, a per-pixel boundary spreading method is presented to improve the image segmentation and provide local regions for image retrieval. Second, a local-region image retrieval is conducted to provide a powerful support for the depth estimation of each segmented part. Third, a scene similarity matrix is constructed and combined with the initial depth prior to establish the correlations across different regions for a consistent depth optimization. Experiments show that applying our method to classic data-driven methods can improve the performance of depth estimation. Besides, our results also manifest clearer depth boundaries in some local regions than the state-of-the-art methods based on deep learning framework.
Deep Recurrent Deterministic Policy Gradient for Physical Control

The observable states play a significant role in Reinforcement Learning (RL), meanwhile, the performance of RL is strongly associated with the quality of inferred hidden states. It is a challenging task to accurately extract hidden states because they are often related to both environment’s and agent’s histories, and require numerous domain knowledge. In this work, we aim to leverage history information to improve the performance of agent. Firstly, we discuss that the neglect and usual process of history information are harmful to agent’s performance. Secondly, we propose a novel model that combines the advantage of both supervised learning and RL. Specifically, we extend the framework of classical policy gradient and propose to extract history information using recurrent neural networks. Thirdly, we evaluate our model in simulated physical control environments, outperforming the state-of-the-art models and performing obviously better on more challenging tasks. Finally, we analyze the reasons and suggest possible approaches to extend and scale up the model.
Simulating Complexity Measures on Imbalanced Datasets

Classification tasks using imbalanced datasets are not challenging on their own. Classification models perform poorly on the minority class when the datasets present other difficulties, such as class overlap and complex decision border. Data complexity measures can identify such difficulties, better dealing with imbalanced datasets. They can capture information about data overlapping, neighborhood, and linearity. Even though they were recently decomposed by classes to deal with imbalanced datasets, their high computational cost prevents their use on applications with a time restriction, such as recommendation systems or high dimensional datasets. In this paper, we use a Meta-Learning approach to estimate the decomposed data complexity measures. We show that the simulated measures assess the difficulty of the dataset after applying preprocessing techniques to different sample sizes. We also show that this approach is significantly faster than computing the original measures, with a statistically similar estimation error for both classes.
Comparison of Multinomial Naïve Bayes Classifier, Support Vector Machine, and Recurrent Neural Network to Classify Email Spams

Information is one of the basic needs of the present. Many services are used as a means to send information messages, one of which is email. With the various conveniences offered by email, causing positive and negative impacts. One of the negative impacts is that an abused email becomes a spam email that has the potential to be detrimental and harm to the recipient. This is the basis for researching to determine the most accurate estimation of spam in emails by comparing the Multinomial Naïve Bayes Classifier algorithm(MNBC), Support Vector Machine algorithm(SVM), and Recurrent Neural Network(RNN) algorithm. The results of the evaluation are presented using the Classification Report to determine the results of accuracy, precision, memory, f1 score of each algorithm used. In this research, the algorithm that produces the greatest accuracy value in spam classification on email is the Support Vector Machine algorithm where the accuracy value of this algorithm is 96%, the precision value is 0.92, recall value is 0.96, and f1-score is 0.94.
AHIAP: An Agile Medical Named Entity Recognition and Relation Extraction Framework Based on Active Learning

Knowledge graph plays a significant role in many domains for providing a wide range of assistance. In the medical domain, clinical guidelines, academic papers, Electronic Medical Records (EMRs) and crawled data from the Internet contain essential information. However, those data are usually unstructured but vital to knowledge graph construction. The construction of knowledge graph using unstructured data requires a large number of medical experts to participate in annotations based on their prior experiences and knowledge. Knowledge graphs' quality highly depends on the performances of medical named entity recognition and relation extraction that are both based on data annotation. However, faced with handling such a large amount of enormous data, manual labelling turns out to be a high labor cost task. Besides, the data is generated rapidly, requiring us to annotate and extract quickly to keep the pace with the data accumulation. Therefore, we propose a named entity recognition and relation extraction framework, AHIAP, to solve these problems mentioned above. AHIAP uses active learning method to reduce the labor cost of the annotation process while maintaining the annotation quality. There are two modules in AHIAP, an active learning module for reducing labor cost and a measurement module to control the quality. By using active learning, AHIAP only takes 200 samples to get to the accuracy of 70%, whereas the standard learning strategy takes 4000 records to get the same accuracy.
Protik: Bangla Sign Language Teaching Aid for Children with Impaired Hearing

The disabled peoples take up a large percentage of our community but their education is often ignored. A big percentage of our population has a hearing disability where most are left illiterate. While the adults grow up to associate some written words or signs with sign language, children have a hard time doing the same thing. As most children with hearing disabilities communicate via sign language only, learning to read and write is more difficult for them than it is for a normal child because of this association of written words with the spoken words used in our day to day lives. In this paper, we propose a model to aid children with hearing disabilities to learn to read and write without intensive guidance. The proposed model con convert PDF of paper books to the corresponding sequence of signs in Bangla sign language, making it easier for the disabled children to associate the words with their usage. The model can be used as an aid to teach the children while also acting as a medium for the children to read various books. The Tesseract OCR has been used to convert the images in the PDF to texts and the sequence of words is generated using one to one mapping. For the PDF to text conversion, 81.75% accuracy has been reported for the OCR model for Bangla.
Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations

Standard approaches to stochastic gradient estimation, with only noisy black-box function evaluations, use the finite-difference method or its variants. Though natural, it is open to our knowledge whether their statistical accuracy is the best possible. This paper argues so by showing that central finite-difference is a nearly minimax optimal zeroth-order gradient estimator for a suitable class of objective functions and mean squared risk, among both the class of linear estimators and the much larger class of all (nonlinear) estimators.
Risk-based compatibility analysis in automotive systems engineering

Software is the new leading factor for innovation in the automotive industry. With the increase of software in road vehicles new business models, such as after-sale updates (i.e., Function-on-Demand) and Over-the-Air-Updates come into focus of manufacturers. When updating a road vehicle in the field, it is required to ensure functional safety. An update shall not influence existing functionality and break its safety. Hence, it must be compatible with the existing software. The compatibility of an update is ensured by testing. However, testing all variants of a highly configurable system, such as a modern car's software, is infeasible, due to the combinatorial explosion. To address this problem, in this paper, we propose a risk-based change-impact analysis to identify system variants relevant for retesting after an update. We combine existing concepts from product sampling, risk-based testing, and configuration prioritization and apply them to automotive architectures. For validating our concept, we use the Body Comfort System case study from the automotive industry. Our evaluation reveals that the concept backed by tool support may reduce testing effort by identifying and prioritizing incompatible variants wrt to a system update.
SQL Injection Attack and Detection Based on GreenSQL Pattern Input Whitelist

With the rapid development of Internet technology, the era of big data is coming. SQL injection attack is the most common and the most dangerous threat to database. This paper studies the working mode and workflow of the GreenSQL database firewall. Based on the analysis of the characteristics and patterns of SQL injection attack command, the input model of GreenSQL learning is optimized by constructing the patterned input and optimized whitelist. The research method can improve the learning efficiency of GreenSQL and intercept samples in IPS mode, so as to effectively maintain the security of background database.
Unbiased Image Style Transfer

Image style transferring process generates an output image in the target style with a specific strength for a given pair of content and target image. Recently, feed-forward neural networks have been employed in this process to fastly decode a linearly interpolated feature in encoded feature space. However, to date, no studies have been conducted to analyze the effectiveness of this style interpolation method. In this article, we tackle the missing work of the in-depth analysis of style interpolation and propose a new method that is more effective in controlling the strength of the desired style. The existing methods are biased because the training of a network is performed with one-sided data of full style strength. Therefore, such methods do not guarantee the generation of a satisfactory output image in an intermediate style strength. To resolve this problem of a biased network, we propose an unbiased learning technique, which uses unbiased training data and loss to allow a feed-forward network to learn the desired regression of style consistent with a specific interpolation function in encoded feature space. The experimental results verified that our unbiased method achieved a better regression learning between style control parameter and output image style, and more stable style transfer that is insensitive to the weight of style loss without adding complexity in image generating process.
Interest of Defect Technical Debt: An Exploratory Study on Apache Projects

Defect technical debt (defect debt) refers to known deferred bugs that have not been fixed. The interest of a defect debt item (i.e., bug) is the extra effort needed to fix the bug due to the delay of fixing. It is important to measure defect debt interest in a software system in order to be aware of how much the interest of certain bugs is and which bugs should be fixed first. Furthermore, it is valuable to understand the features of the bugs of high interest or no interest, so as to facilitate the identification of those kinds of bugs. In this work, we proposed three pairs of measures for quantifying defect debt interest at three granularities (i.e., lines of code, source file, and package) of software changes, and conducted an exploratory case study on 13,438 bugs collected from 59 non-trivial Apache open source software projects written mainly in Java. We have the following findings. (1) Each bug, with an average of 224 days delay of fixing, has interest of 660 to 845 lines of code, 0.57 to 1.05 source files, and 0.29 to 0.35 package on average in need for understanding or modifying when fixing the bug. (2) The average interest of a bug shows overall increasing trends over delayed time of bug fixing and bug priority levels from the lowest to highest. (3) Around 30% of the bugs under study did not incur interest at the granularity of lines of code, which means that the involved source files in the bug-fixing commits of such bugs were not modified at all during the delayed time of bug fixing. (4) The average code change size of bug-fixing commits of a bug without interest is much smaller than that of a bug with (high) interest; for a bug without interest at the granularity of lines of code, the source files involved in the bug-fixing commits of the bug were alternately modified by different committers much less frequently than those of the bug with (high) interest.
Semantic Analysis for Conversational Datasets: Improving Their Quality Using Semantic Relationships

As more and more datasets become available, their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially, in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task- and topic-specific conversational datasets to be used for training conversational agents. The platform explores available conversational datasets, extracts information based on semantic similarity and relatedness, and applies a weight-based score function to rank the information based on its value for the specific task and topic. The finalized dataset can then be used for the training of an automated conversational assistance over accurate data of high quality.
In-Database Graph Analytics with Recursive SPARQL

Works on knowledge graphs and graph-based data management often focus either on graph query languages or on frameworks for graph analytics, where there has been little work in trying to combine both approaches. However, many real-world tasks conceptually involve combinations of these approaches: a graph query can be used to select the appropriate data, which is then enriched with analytics, and then possibly filtered or combined again with other data by means of a query language. In this paper we propose a language that is well-suited for both graph querying and analytical tasks. We propose a minimalistic extension of SPARQL to allow for expressing analytical tasks over existing SPARQL infrastructure; in particular, we propose to extend SPARQL with recursive features, and provide a formal syntax and semantics for our language. We show that this language can express key analytical tasks on graphs (in fact, it is Turing complete). Moreover, queries in this language can also be compiled into sequences of iterations of SPARQL update statements. We show how procedures in our language can be implemented over off-the-shelf SPARQL engines, with a specialised client that can leverage database operations to improve the performance of queries. Results for our implementation show that procedures for popular analytics currently run in seconds or minutes for selective sub-graphs (our target use-case).
IT education as a factor to influence gender imbalances in computing: Comparing Russian and American experience.

Introduction. The problem of the relatively small number of women professionally employed in computing (computer science and information technology) is relevant throughout the world. Despite the fact that IT professionals are widely in demand, women in many countries, including theUSA andRussia, make up no more than a quarter of their total number, which requires explanation. One of the major reasons for this phenomenon, according to the authors, lies in the education system. The aim of this article was to analyse the factors affecting gender imbalance in IT professions, by comparing two countries in which information technology has historically played an important role, and which are very different from each other in many ways – economic, political, educational system and others. Research methodology. The present research is based on the comparison of data on IT education in schools and universities, and the degree of involvement of girls and women in computing in theUSA andRussia. Results. Both in theUSA and inRussia, gender imbalances in IT professions are formed largely in the field of education. Cultural stereotypes about computing as a male-dominated profession are produced by the media. Such stereotypes can discourage some girls and young women from studying computer science and also result in imbalance formation. The education system needs to increase the confidence of girls and young women in the possibilities of realising their abilities in the field of computer science and information technologies. Educational institutions should help to eliminate the negative attitude towards girls’ choice of IT professions. Scientific novelty. For the first time, general factors in the field of education were identified that affect gender imbalances among IT professionals inRussia and theUSA – the countries with significantly different traditions and educational systems. Practical significance of the present work is to justify the conditions for improving school and university education to solve the problem of gender inequality in IT industry.
Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together

Multi-agent path finding in formation has many potential real-world applications like mobile warehouse robotics. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Further-more, they are usually centralized planners and require the whole state of the environment. Other decentralized partially observable approaches to MAPF are reinforcement learning (RL) methods. However, these RL methods encounter difficulties when learning path finding and formation problems at the same time. In this paper, we propose a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi-objective task into unrelated ones. It also calculates a theoretical weight that makes each tasks reward has equal influence on the final RL value function. Additionally, we introduce a communication method that helps agents cooperate with each other. Experiments in simulation show that our method outperforms other end-to-end RL methods and our method can naturally scale to large world sizes where centralized planner struggles. We also deploy and validate our method in a real-world scenario.
Edge to Cloud End to End Solution of Visual Based Gun Detection

Abstract There are over a half million cases of gun violence globally every year. State of the art video surveillance requires human to watch the video footage to monitor gun violence. In this paper, we present an AI based edge to cloud solution appropriate for smart surveillance and control. Firstly, we build the key training dataset by harvesting the publicly available videos and movies. Then we design three deep convolution neural networks (CNNs) models, there are two models running on the edge device, which is a video camera, and the third one runs on the cloud. By distributing these three models into edge and cloud, we are able to achieve a good gun detection system that can be deployed in real life.
Image Encryption Algorithm Based on Logistic Chaotic Scrambling System

In order to ensure the confidentiality of image in transmission, a two-dimensional Logistic chaotic system with wide key space is constructed, which integrates with the random scrambling encryption algorithm, and scrambles the original image by optimizing the discrete chaotic sequence to form the encrypted image. The simulation results show that the algorithm is easy to implement, highly efficient, sensitive to the initial condition disturbance of the system, and it has large key space, good security and strong anti-attack ability.
AONet: Active Offset Network for crowd flow prediction

Predicting crowd flow is of great importance to public safety and traffic management. The crowd flow is difficult to predict accurately and timely due to the uncertainty of the future positions. In this paper, we propose a novel Active Offset Network (AONet), in which ActiveGRU (Active Gate Recurrent Unit) is designed to predict the variation of pedestrians’ positions in the crowd flow. Its inner location-variant recurrent structure is implemented by utilizing convolution operation on low dimensional spatio-temporal sequences to obtain fractional offset locations. Afterwards, the sampling locations are determined by bilinear interpolation on fractional offset locations. Moreover, a probabilistic sparse strategy is introduced to reduce the links between sampling locations during supervised training. Finally, the experiments over popular benchmarks demonstrate that our method can actively characterize the future positions of pedestrians. Meanwhile, the performance of the proposed AONet is superior over state-of-art baselines with regard to both accuracy and computational savings.
Towards Vision-based Analysis of Indoor Trajectories for Cognitive Assessment

The rapid increase of the senior population in our societies calls for innovative tools to early detect symptoms of cognitive decline. To this aim, several methods have been recently proposed that exploit Internet of Things data and artificial intelligence techniques to recognize abnormal behaviors. In particular, the analysis of position traces may enable early detection of cognitive decline. However, indoor movement analysis introduces several challenges. Indeed, indoor movements are constrained by the ambient shape and by the presence of obstacles, and are affected by variability of activity execution. In this paper, we propose a novel method to identify abnormal indoor movement patterns that may indicate cognitive decline according to well known clinical models. Our method relies on trajectory segmentation, visual feature extraction from trajectory segments, and vision-based deep learning on the edge. In order to avoid privacy issues, we rely on indoor localization technologies without the use of cameras. Preliminary experimental results with a real-world dataset gathered from cognitively healthy persons and people with dementia show that this research direction is promising.