Reviewerly/exHarmony huggingface dataset:
Feature Learning in Video-Based Analysis of Animal Emotional States

There is a growing interest in animal-related disciplines and in ACI in applying AI-based approaches for studying animal behavior. Most efforts have so far been focused on automatic tracking of animal movement, and recognition of some basic postures, behaviors and activities. However, AI has great potential not only for automatically “seeing” the animal, but also for “understanding” and even “explaining” its behavior. More specifically, deep learning techniques have an increasingly efficient performance in a variety of tasks related to image and video classification. In this research we wish to take these techniques one step further, harnessing their key idea of feature learning in the context of animal behavior. Namely, by investigating the learnt features of deep learning classifiers, we provide a method for extracting insights on how classification is performed, hopefully leading to new insights into animal behavior.
Asynchronous collaborative learning across data silos

Machine learning algorithms can perform well when trained on large datasets. While large organisations often have considerable data assets, it can be difficult for these assets to be unified in a manner that makes training possible. Data is very often 'siloed' in different parts of the organisation, with little to no access between silos. This fragmentation of data assets is especially prevalent in heavily regulated industries like financial services or healthcare. In this paper we propose a framework to enable asynchronous collaborative training of machine learning models across data silos. This allows data science teams to collaboratively train a machine learning model, without sharing data with one another. Our proposed approach enhances conventional federated learning techniques to make them suitable for this asynchronous training in this intra-organisation, cross-silo setting. We validate our proposed approach via extensive experiments.
TeGraF

Detection of fraudulent transactions is an imperative research area in the financial domain, affecting the different entities involved in the payment process. An accurate fraud detection algorithm will help in identifying fraudulent transactions, thus facilitating immediate response and dispute resolution. To this effect, this research proposes a novel framework TeGraF for detecting fraudulent transactions by modeling temporal and structural features from a given input. The proposed algorithm operates at the intersection of two key research areas: Temporal Point Processes (TPPs) and Graph Neural Networks (GNNs). Due to the wide occurrence of sequential data in the financial domain, TPPs are very useful for modeling the sequence of transactions. Parallelly, the financial domain data can also be represented as a graphical structure capturing interactions between users and vendors/merchants. Thus, the proposed algorithm utilizes the temporal features learned via the TPP based model and the structural features captured via the GNN for modeling fraudulent transactions. Different graph representation learning techniques like Node2Vec, Metapath2Vec, LINE, DeepWalk, and BiNE are employed to compare the overall performance. Experiments have been evaluated on a synthetic dataset containing 62K users and 4M transactions, which demonstrate the improved performance of the proposed technique as compared to the existing algorithms.
Privacy-preserving portfolio pricing

Investment banks offer the service of purchasing an entire portfolio from a client in a single transaction. The transaction enables the client to remove unwanted assets from their books, and provides immediate cash to be used in other investments. The bank usually offers a price that is slightly lower than the market price, therefore potentially gaining in this transaction when selling its content later in the market, or by internalizing the portfolio. Even though liquidating the portfolio is advantageous for the client, they are concerned about information regarding their strategies or holdings "leaking" and they would also like to be able to "shop" the portfolio to multiple banks. The current industry practice calls for the client to provide a summarized description of the portfolio without full details of the constituents, leading to imprecise pricing by the banks. We propose a new way for pricing portfolios by adapting secure computation to this domain. Our approach allows the client to maintain secrecy regarding the constituents of the portfolio, while enabling the bank to provide a fair price. We study several metrics for pricing portfolios and provide a suite of two-party protocols for computing those metrics, which are all provably secure. We test our protocols and show their scalability experimentally. We believe that this privacy-preserving pricing method offers the potential to transform the practice of portfolio pricing.
AuthSHAP

Adversarial archetypes exploit the workings of any system to disrupt the robustness and the decision-making of the underlying machine learning algorithms. This is an important area of concern in the banking industry where the global approbation of real-time & friction less payment systems has prompted the financial institutions to invest in superior authentication solutions. Identifying fraudulent actors poses an inherent challenge for varied reasons - thorough fraud detection can have an impact on customer satisfaction. While fraud detection slows down the authentication process, the customers' demands faster response. Present systems tend to produce numerous false positives, along with being vulnerable to ingenious fraudsters, which in conjunction with dollar loss, also leads to reputation loss for the issuers. In this paper, we present AuthSHAP, a first of its kind, model agnostic and robust implementation of SHAP (SHapley Additive exPlanations) to uncover the extent to which key features are not appropriated by any model in the decision making. This 'knowledge' is significant information to a fraudsters to design intelligent or adversarial attacks. We show that even in black-box settings, where the attacker only needs to have a fair process or business understanding about the type of information or raw features that are passed through the system and the responses it is possible to understand the vulnerability. Thus, this can be used by any financial institution as an active preventive measure (a) to shield their authentication system from adversarial attacks and, also (b) to reduce false declines to prevent a sub-optimal customer experience amounting to both revenue and reputation loss for entities across the payment value chain. We propose an evaluation method using a simulation where we create a decision system or Model with desired vulnerability and identify the same via our proposed methodology in a black-box setting. We extend this by performing several experiments on aggregated and anonymized real-world financial transaction data and validate our findings with internal subject matter experts.
Automatic Feature Engineering Using Self-Organizing Maps

Feature Engineering (FE) consists of generating new, better features to improve the results obtained by Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose an automatic FE approach based on Self-Organizing Maps (SOM) in which new features are generated via pattern recognition. The use of the SOM algorithm in variable generation tasks can identify data elements that help Machine Learning models to obtain better results and points out to a broad direction for future researches.
Towards explainable artificial intelligence for the leukemia subtype recognition

In this work, we provide a solution to the leukemia subtype recognition problem. Most approaches used for solving a variety of pattern recognition problems have a drawback: in general, they lack explainability. In this paper, we provide a solution for facing this situation. We describe a model whose stages allow deriving knowledge for solving the leukemia subtype recognition problem, are intelligible for the user. Results show that multiclass recognition is achieved from the solutions obtained by the model through multiple runs.
Improving Neural Style Transfer by Incorporating Mid-Level Representation

Gatys et al. [1] has demonstrated that convolutional neural networks (CNN) are able to separate the style and content of images and then recombine them into an artistic image. This process is known as neural style transfer (NST) and has had repercussions in both academia and industry. This paper covers the approach based on image optimization that allows the selection of artistic styles arbitrarily; however, the process has a high computational cost. For this reason, it is proposed to make some modifications that allow performing the neural style transfer with a lower computational cost, in addition to adding a similarity indicator that determines whether the synthesized image has reached the desired style using mid-level representation techniques. These modifications have given better visual qualitative results, in addition, the execution time of an iteration has improved up to 1.53× times compared to other similar methods, and with the similarity indicator, it has been possible to reduce the number of iterations necessary to obtain the synthesized image.
Semantic expansion to improve diversity in query formulation

Although the diversity of results has been studied since the early information retrieval systems, few studies explore diversity and its representation in an educational context. Inherently, approaches that seek to address difficulties in web search are focused on maximizing the relevance of results over the original query. This work presents a method that integrates semantic relationships using Word Embedding for expansion with blind feedback to improve diversity. Using a corpus based on the user’s query logs from a realistic setting, three Word2vec models are trained to obtain semantically relevant terms for each naturally elaborated query by students. The proposed architecture is studied in a specific search task, limiting the number of candidate terms in each model according to the allowed frequency of words. Finally, the diversity in two groups of queries is compared, measuring the lexical similarity of the snippets of the results pre-expansion and post-expansion. Results indicate the potential for improving diversity, also showing that lower semantic similarity can lead to better diversity. Therefore, we provide a method to improve learning through web searches.
A Model for Selecting Relevant Topics in Documents Aimed at Compliance Processes

This paper proposes a semantic Natural Language Processing (NLP) approach used to assist in the automated characterization of information relevant to compliance activities. In this context, the proposed model combines two topic modeling techniques: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), the first used to assist in the dimensionality reduction process, while the second, used to identify the number of relevant topics addressed in the processed data. Interesting results were achieved when three large databases tested the model, namely: Database of European Laws (period 1952 to 1990), Database of Audit reports issued by the State General Secretariat of Management of Pernambuco (period 2010 to 2019), and Database of Appellate Decisions issued by the Brazilian Federal Accountability Office (year of 2019). We compared the performance of three machine learning methods: K-means, LSA and LDA. In our experiments, we observed that (i) pre-processing techniques have a marked influence on the result of topic extraction; that (ii) Silhouette techniques and topic coherence produced the best value for the quantitative topics in all databases ; that LSA associated with LDA presented the best performance in all three databases, regarding the identification of relevant themes (topics) identified. Overall, the best results were obtained using the database in English (European Union Laws).
Use of Fitness Sharing in the Local Rule-Based Explanations Method

Recent machine learning algorithms present remarkable results in many problems. However, the decisions made by some of these algorithms are very often difficult for human experts to interpret. Some recent works in the literature try to minimize this disadvantage, proposing algorithms that explain the decisions taken by any black-box model. One of these models is the Local Rule Based Explanations (LORE), that generates local explanations by using a Decision Tree (DT) that locally reproduces the decision boundaries of the black-box model. In LORE, the DT is trained by using an artificial dataset generated by a standard Genetic Algorithm (GA). In this paper, we show that the GA employed in LORE does not necessarily preserve the diversity of solutions in the final population. The diversity of the population is important to generate DTs that can more accurately reproduce the decision boundaries of the black-box model close to the instance to be explained. We then propose the use of fitness sharing in the GA in order to preserve the diversity of the population and generate local decision boundaries of the DT more similar to those of the black-box model. Experimental results show that LORE with fitness sharing generally produces better local explanations.
Industrial case study of causal modeling of continuous casting and lamination of steel tubes

Black-box models have shown high flexibility and accuracy in prediciting what values certain variables involved in industrial processes will assume in the future, given the values of certain other variables. These models, however, are frequently too complex to be interpreted by a human operator, and are frequently unable to furnish adequate answers to queries regarding interventions in a given system, or to answer counterfactual queries. Causal models, however, frequently can. In this work we explore the causal modeling of two stages in the production of seamless steel tubes, extracting directed acyclic graphs, which can then be used for rule extraction, as well as for predictive, intervention and counterfactual queries.
Using Deep Learning Predictors and Latent Dirichlet Allocation to Identify Key Issues Affecting Clients in Chilean Restaurants

In this work, we use a general sentiment analysis methodology to train different Deep Learning predictors so that a proper quantitative valuation can be obtained based on qualitative information given by customers on social media (comments). We use Convolutional Neural Network and Long Short-Term Memory combined with different inputs including the body of the comments and its title. With the trained predictors we classify a large set of comments regarding negative positive customer experiences. Finally we use Latent Dirichlet Allocation algorithm to identify the specific issues appearing on the comments related to negative customer experience ratings.
Vehicle Segmentation with Coarse Distance Estimation Based on Monocular Vision

With the increasing population of drivers and the popularity of intelligent cars, traffic safety is becoming more concerned. Aiming to reduce the probability of road accidents, many works have explored how to provide an early warning when the distances between the current car (or the in-car camera) and vehicles ahead are dangerous. In this paper, benefiting from the robust pixel-level understanding of cutting-edge deep networks, we introduce a vehicle segmentation method based on monocular vision, detecting vehicles in front of the current one and generating a segmentation mask for each vehicle instance detected. Instead of concentrating our efforts on measuring distance precisely, we categorize distances into three classes (“near”, “medium”, and “far”), which are pretty enough for an early collision warning. Our model is trained and evaluated on our hand-made dataset, after which we transfer and deploy it onto an Android TV virtual machine to test its performance. All the results we got show that our method does a good job in vehicle segmentation with coarse distance estimation, being applicable to aided driving.
HAEE: Question Classification Using Hierarchical Intra-Attention Enhancement Encoder

With the development of E-commerce, an Automated Question-Answering system takes a crucial part in customer service. Question classification, which assigns labels to questions according to the answer types, is one of the tasks in question answering. Previous methods usually used handcraft features like named entity recognition, but it needs the predefined dictionary or tools. The machine learning approaches are recently applied to this task and achieve high accuracy. In this paper, we proposed HAEE, a Hierarchical intra-Attention Enhancement Encoder which composed of bidirectional GRUs and intra-attentions. In addition, we adopt the character input to address the issue of the OOV (Out-Of-Vocabulary) problem and create multiple intra-attentions to simulate the certain relationships between characters (Chinese) or words (English) to enhance the influence of tokens on the sentence. We evaluate the HAEE model in an actual corporate setting and several datasets. As shown in the experimental results, our HAEE model outperforms the existing state-of-the-art models on question classification tasks, especially for the Chinese corpus.
Thwart Physical and Digital Domain's Adversarial Attack Methods on Face Detection

Face detection is a classic problem widely focused on the field of computer vision. It has essential values in security monitoring, human-computer interaction, social interaction, and other fields. Face detection technology has been widely integrated into digital cameras, smartphones, and other end-to-end devices to realize the functions of finding out and focusing on faces. For example, beauty camera applications use face detection to identify faces in preparation for subsequent beauty functions. Face recognition relies on face detection to provide support and assurance. Unfortunately, face detection security problems are constantly emerging in the public&#x0027;s vision with the widespread use of face detection technology. Research on attacking and defending methods on face detection has become a hot research topic about artificial intelligence security. By studying the adversarial attack methods on face detection, we can better evaluate the face detection models&#x0027; security, and at the same time, can give beneficial help to improve the security of face detection. Among these methods, the most popular attacking method is adversarial attacks. In this paper, we have rationalized and classified the methods of adversarial attacks on face detection according to the attacking principles, the attacking domain, and the attacker&#x0027;s understanding of the face detection models. According to the domain to make classification, it includes digital-domain attack, physical-domain attack; according to the attacker&#x0027;s understanding of the face detection models, it includes black-box attack, white-box attack, grey-box attack. Finally, according to the problems in its current development situation, we proposed the possible solutions and predicted its future development trend.
Mirror Feature Fusion for Salient Object Detection

The mainstream salient object detection method often fuse and refine at the feature level to achieve saliency object detection at present. However, different features play different roles in salient object detection due to the differences between the feature of each layer of the depth model, so simple fusion of the feature of each layer will affect the performance of the system. In view of the above observation, we propose a mirror fusion network (MFNet) in the paper, which is mainly composed of low-level denoising module (LDM), refinement module (RM), and mirror fusion module (MFM). With the assistance of three algorithm modules, not only the multi-level features can be fused effectively, but also the detailed object boundary can be obtained. We take advantage of a hybrid loss function to measure the system loss when the model is training. Experiments on five benchmark datasets show that the proposed method is superior to seven related deep salient object detection methods under three evaluation metrics.
Knowledge Distillation based Lightweight Adaptive Graph Convolutional Network for Skeleton-based action recognition

Skeleton-based human action recognition has received extensive attention due to its easy access to human skeleton data. However, the current mainstream skeleton-based action recognition methods have more or less the problem of overlarge parameters, which makes it difficult for these methods to meet the requirements of timeliness and accuracy. To solve this problem, we improve attention-enhanced adaptive graph convolutional neural network (AAGCN) to obtain a high-precision improved AAGCN (IAAGCN), and use it as teacher model to conduct knowledge distillation of our lightweight IAAGCN (LIAAGCN). The results of the tests on the NTU-RGBD dataset are validated by knowledge distillation to allow LIAAGCN to maintain good accuracy while keeping the parameters small.
Course Design of JSP Programming Based on Advanced Project-Driven Method

There is a huge gap between theory and practice in the traditional teaching model, which seriously degrades student's practical ability and problem-solving ability. To overcome the shortcomings of the traditional teaching model, this paper proposes a novel application design based on advanced project-driven in the teaching process of JSP courses, which is capable of simultaneously boosting students' theoretical and practical abilities. Particularly, the teaching knowledge points are decomposed into dual tasks involving theoretical and practical goals by following the way of “project advanced guidance, task decomposition driven”. Compared with the traditional teaching model, splitting the knowledge points into the project during the teaching process can not only improve the learning efficiency but also cultivate the students' computer practice ability. The research results verify that our proposed teaching model allows students to deepen their knowledge mastery, establish a complete knowledge system, improve their practical ability, and finally apply theoretical knowledge to solve practical problems in the real world.
A semantic relatedness measurement method based on domain ontology considering all relationships

The rapid expansion of information and knowledge requires a various applications in computational linguistics and artificial intelligence employing semantic similarity to solve challenging tasks, such as information retrieval, text classification, machine translation, document clustering and so on. Today ontology is an extremely essential approach mainly used to represent acquired knowledge as well as to ensure data and knowledge integration. In this situation, a comprehensive semantic similarity measure is required. We propose a semantic relatedness calculation method that considers all the relationships including common and different attributes relations between concepts. To demonstrate the benefits of exploiting all the relations in domain ontology, we use four method for comparison. The results of our proposed method demonstrate an improvement over the benchmark semantic similarity methods.