Reviewerly/exHarmony huggingface dataset:
Deep Attention Network for RGB-Infrared Cross-Modality Person Re-Identification

Abstract RGB-Infrared cross-modality person re-identification is an important task for 24hour full-time intelligent video surveillance, the task is challenging because of cross modal heterogeneity and intra modal variation. A novel deep attention network is proposed in this paper to handle these challenges by increasing the discriminability of the learned person features. The method includes three elements: (1) dual-path CNN to extract the feature maps of the RGB images and infrared images respectively, (2) dual-attention mechanism combining spatial attention and channel attention to enhance the discriminability of extracted features, and (3) joint loss function joining bi-directional ranking loss and identity loss to constraint the training process to further increase the accuracy. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed method because the method achieves higher performance than state-of-the-arts methods.
Spark Structured Streaming: Customizing Kafka Stream Processing

The aim of the present paper is to develop an improvement of large-scale multi-party data exchange and stream processing solution. The method of choice uses Apache Kafka streams as well as HDFS file granulation, and is exemplified in a real project of data ingestion into the Hadoop ecosystem. The management and conditional stream controlling procedures are proposed. Various ways to manage Kafka offsets during stream processing are considered.
On the Fairness of Privacy-Preserving Representations in Medical Applications

Representation learning is an important part of any machine learning model. Learning privacy-preserving discriminative representations that are invariant against nuisance factors is an open question. This is done by removing sensitive information from the learned representation. Such privacy-preserving representations are believed to be beneficial to some medical and federated learning applications. In this paper, a framework for learning invariant fair representations by decomposing the learned representation into target and sensitive codes is proposed. An entropy maximization constraint is imposed on the target code to be invariant to sensitive information. The proposed model is evaluated on three applications derived from two medical datasets for autism detection and healthcare insurance. We compare with two methods and achieve state of the art performance in sensitive information leakage trade-off. A discussion regarding the difficulties of applying fair representation learning to medical data and when it is desirable is presented.
TPR: Text-aware Preference Ranking for Recommender Systems

Textual data is common and informative auxiliary information for recommender systems. Most prior art utilizes text for rating prediction, but rare work connects it to top-recommendation. Moreover, although advanced recommendation models capable of incorporating auxiliary information have been developed, none of these are specifically designed to model textual information, yielding a limited usage scenario for typical user-to-item recommendation. In this work, we present a framework of text-aware preference ranking (TPR) for top- recommendation, in which we comprehensively model the joint association of user-item interaction and relations between items and associated text. Using the TPR framework, we construct a joint likelihood function that explicitly describes two ranking structures: 1) item preference ranking (IPR) and 2) word relatedness ranking (WRR), where the former captures the item preference of each user and the latter captures the word relatedness of each item. As these two explicit structures are by nature mutually dependent, we propose TPR-OPT, a simple yet effective learning criterion that additionally includes implicit structures, such as relatedness between items and relatedness between words for each user for model optimization. Such a design not only successfully describes the joint association among users, words, and text comprehensively but also naturally yields powerful representations that are suitable for a range of recommendation tasks, including user-to-item, item-to-item, and user-to-word recommendation, as well as item-to-word reconstruction. In this paper, extensive experiments have been conducted on eight recommendation datasets, the results of which demonstrate that by including textual information from item descriptions, the proposed TPR model consistently outperforms state-of-the-art baselines on various recommendation tasks.
Implementation of Blockchain for Secure Bank Transactions

Blockchain is a distributed database or a decentralized ledger which is most commonly used to exchange digital currency and perform transactions securely. Every participant of the network has access to the ledger which will be updated by every new transaction. The Blockchain ledger is a collection of all transactions executed in the past. The Blockchain ledger is a continuously growing tamper-proof data structure containing blocks that hold batches of individual transactions. The completed blocks are added in chronological order. Blockchain via Bitcoin has had a massive impact on the world in the past decade and it's safe to say that this will continue, especially with many people working tirelessly to remove the various limitations that are prohibiting blockchains from becoming mainstream. One such limitation is the high processing and electrical costs that come from the Proof-of-Work consensus protocol. In this paper, we propose an alternative proof-by-approval protocol which is a more advanced form of the proof-of-reputation protocol, that offers better security and is a more decentralized approach than the former at the cost of being less performant and harder to setup.
Reconstruction-based anomaly detection for the cloud

The detection of anomalies in cloud metrics is an important way to identify suspicious data instances that indicate a system problem such as hardware failures, performance bottlenecks or intrusions. Yet, especially in a cloud computing infrastructure where the amount and variety of services is constantly increasing, it is getting more and more challenging to monitor and maintain the system manually. Thus, it is beneficial to use machine learning to detect anomalies at least partially in an automated way. The contribution of this paper is two-folded: firstly, we evaluate three unsupervised, reconstruction-based methods for anomaly detection (PCA, Autoencoder, LSTM-Encoder-Decoder) on the Yahoo! Webscope S5 benchmark dataset. Secondly, we compare our chosen models to a widely-used density-based approach and show that our reconstruction-based approaches outperform the related work.
Player Behavior Profiling through Provenance Graphs and Representation Learning

Arguably, player behavior profiling is one of the most relevant tasks of Game Analytics. However, to fulfill the needs of this task, gameplay data should be handled so that the player behavior can be profiled and even understood. Usually, gameplay data is stored as raw log-like files, from which gameplay metrics are computed. However, gameplay metrics have been commonly used as input to classify player behavior with two drawbacks: (1) gameplay metrics are mostly handcrafted and (2) they might not be adequate for fine-grain analysis as they are just computed after key events, such as stage or game completion. In this paper, we present a novel approach for player profiling based on provenance graphs, an alternative to log-like files that model causal relationships between entities in game. Our approach leverages recent advances in deep learning over graph representation of player states and its neighboring contexts, requiring no handcrafted features. We perform clustering on learned nodes representations to profile at a fine-grain the player behavior in provenance data collected from a multiplayer battle game and assess the obtained profiles through statistical analysis and data visualization.
An ontology-based knowledge representation in the field of cognitive functions assessment

Abstract Ontology-based knowledge representation techniques are used today as well-proven tools supporting decision making. The paper proposes OWL ontology which accumulates knowledge about cognitive functions and methods for assessing cognitive functions. A brief overview of using the ontologies capabilities for representation of knowledge about cognitive functions assessment methods is given. An idea of the structure of ontology is presented. A certain set of class expression axioms and object property axioms also are given. The ontology is implemented in the Protégé 5.2 environment.
Application of augmented reality in aviation

The main duty of the cabin crew is to ensure the safety of all passengers onboard and are crucial during emergency situations. It is mandatory for cabin crew to attend the annual Emergency Procedures Training (EPT) to be able to operate as cabin crew regardless of the seniority. Despite the high importance, this training can be long and bulky leading to boredom and lack of engagement, hence jeopardizing the importance of in-flight safety procedures. Although Augmented Reality (AR) can potentially address this issue while enhancing engagement and learning retention, limited work has been undertaken to apply this technology to EPT. As such, this paper investigates whether augmented reality can effectively improve user engagement during emergency procedures training in the context of aviation. In this endeavor, an AR-based application was developed and is presented in this paper. The Positive Engagement Evaluation Method (PEEM) was then used to assess engagement among the 45 cabin-crew of the national carrier. From the PEEM matrix, the positive engagement score obtained was 10.58 and mean scores from the questionnaire ranged from 3 to 4.7. This highlights that Augmented Reality has the potential to enhance the motivation and engagement of users during the emergency procedures training, although a few limitations were identified.
Incremental maintenance of overgrounded logic programs with tailored simplifications

Abstract The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative generation of ground programs thus achieving a significant payoff in terms of computing time. However, this may require some additional amount of memory and/or the manual addition of operational directives in the declarative knowledge base at hand. We introduce a new strategy for generating series of monotonically growing propositional programs. The proposed overgrounded programs with tailoring (OPTs) can be updated and reused in combination with consecutive inputs. With respect to earlier approaches, our tailored simplification technique reduces the size of instantiated programs. A maintained OPT slowly grows in size from an iteration to another while the update cost decreases, especially in later iterations. In this paper we formally introduce tailored embeddings, a family of equivalence-preserving ground programs which are at the theoretical basis of OPTs and we describe their properties. We then illustrate an OPT update algorithm and report about our implementation and its performance.
Methods for Lines and Matrices Segmentation in RNN-based Online Handwriting Mathematical Expression Recognition Systems

Modern applications for handwritten mathematical expressions recognition are not limited to single expression input and rather provide the possibility to input several expressions at once, complex elements such as equation systems and matrices, and also support edit modifications. In this paper, we examine segmentation of mathematical expressions and present new techniques: multi-line segmentation method using special Dynamic Threshold Distance and matrix segmentation method based on projection profiling modification. The proposed methods are used for the preliminary structure analysis and utilize only geometrical features of the input strokes, therefore they can be used at the first stage of the recognition workflow. By incorporating these methods into our recognition system we make it possible to recognize multi-line expressions and matrices. The evaluation of the segmentation itself and overall recognition accuracy is performed using open benchmark CROHME datasets and in-house datasets and for matrices it demonstrates segmentation success rate 93.66% on CROHME2016. The proposed methods can be applied for other applications such as analysis of handwritten document layout, tables and charts recognition.
Do You Just Discuss or Do You Solve?

Software development is a very cooperative and communicative task. In most software projects, meetings are a very important medium to share information. However, these meetings are often not as effective as expected. One big issue hindering productive and satisfying meetings is inappropriate behavior such as complaining. In particular, talking about problems without at least trying to solve them decreases motivation and mood of the team.
EM-Sign: A Non-Contact Recognition Method Based on 24 GHz Doppler Radar for Continuous Signs and Dialogues

We studied continuous sign language recognition using Doppler radar sensors. Four signs in Chinese sign language and American sign language were captured and extracted by complex empirical mode decomposition (CEMD) to obtain spectrograms. Image sharpening was used to enhance the micro-Doppler signatures of the signs. To classify the different signs, we utilized an improved Yolov3-tiny network by replacing the framework with ResNet and fine-tuned the network in advance. This method can remove the epentheses from the training process. Experimental results revealed that the proposed method can surpass the state-of-the-art sign language recognition methods in continuous sign recognition with a precision of 0.924, a recall of 0.993, an F1-measure of 0.957 and a mean average precision (mAP) of 0.99. In addition, dialogue recognition in three daily conversation scenarios was performed and evaluated. The average word error rate (WER) was 0.235, 10% lower than in of other works. Our work provides an alternative form of sign language recognition and a new approach to simplify the training process and achieve a better continuous sign language recognition effect.
Cross-Domain Recommendation with Adversarial Examples

Cross-domain recommendation leverages the knowledge from relevant domains to alleviate the data sparsity issue. However, we find that the state-of-the-art cross-domain models are vulnerable to adversarial examples, leading to possibly large errors in generalization. That’s because most methods rarely consider the robustness of the proposed models. In this paper, we propose a new Adversarial Cross-Domain Network (ACDN), in which adversarial examples are dynamically generated to train the cross-domain recommendation model. Specifically, we first combine two multilayer perceptrons by sharing the user embedding matrix as our base model. Then, we add small but intentionally worst-case perturbations on the model embedding representations to construct adversarial examples, which can result in the model outputting an incorrect answer with a high confidence. By training with these aggressive examples, we are able to obtain a robust cross-domain model. Finally, we evaluate the proposed model on two large real-world datasets. Our experimental results show that our model significantly outperforms the state-of-the-art methods on cross-domain recommendation.
ParaDis and Démonette – From Theory to Resources for Derivational Paradigms

In this article, we trace the genesis of the French derivational database Démonette and show how its architecture and content stem from recent theoretical developments in derivational morphology and from user needs.The development of this large-scale resource began a year ago as part of the Demonext project.Its conception is grounded in a theoretical approach where the lexemes are connected by derivational relations within derivational families which in turn fit into paradigms.More precisely, Démonette is a partial implementation of ParaDis, a paradigmatic model of morphological representation designed for the description of regular processes and of form-meaning discrepancies.The article focuses on the principles that govern the morphological, structural and semantic encoding of morphologically complex lexemes in Démonette and illustrates the range of form-meaning discrepancies with a variety of examples of non-canonical word formations.
Detecting and Recognizing Outliers in Datasets via Linguistic Information and Type-2 Fuzzy Logic

Abstract Uncertainty appearing in datasets (stochastic, linguistic, of measurements, etc.), if not handled properly, may negatively affect information analysis or retrieval procedures. One of possible methods of dealing with uncertain (rare, strange, unexampled) data is to treat them as “outliers” or “exceptions”. Among different definitions and algorithms for detecting outliers, we are especially interested in those based on linguistic information represented with type-2 fuzzy logic. We introduce new definitions of outliers in datasets in terms fuzzy properties and linguistically expressed quantities of objects possessing them. Next, new algorithms for detecting outlying objects are presented, to answer whether outliers appear in a dataset or not. Finally, recognition algorithms are presented and exemplified to enumerate particular objects being outliers (e.g., to eliminate them for further considerations). The novelty of this contribution is that we define, detect and recognize outliers using linguistic information represented mostly by type-2 fuzzy sets and logic (if any other information like measures or distances is not accessible), and we supersede this way some earlier approaches based on similar but relatively limited assumptions.
Closed-Form Models for Collaborative Filtering with Side-Information

Recent work has shown that, despite their simplicity, item-based models optimised through ridge regression can attain highly competitive results on collaborative filtering tasks. As these models are analytically computable and thus forgo the need for often expensive iterative optimisation procedures, they are an attractive choice for practitioners. We study the applicability of such closed-form models to implicit-feedback collaborative filtering when additional side-information or metadata about items is available. Two complementary extensions to the easer paradigm are proposed, based on collective and additive models. Through an extensive empirical analysis on several large-scale datasets, we show that our methods can effectively exploit side-information whilst retaining a closed-form solution, and improve upon the state-of-the-art without increasing the computational complexity of the original easer approach. Additionally, empirical results demonstrate that the use of side-information leads to more “long tail” items being recommended, benefiting the recommendations’ coverage of the item catalogue.
The choice of informative features based on heterogeneous functionals

Abstract One of the classical problems of the theory of pattern recognition is to reduce the dimension of the original feature space from dimension N to dimension N ’, N ’ &lt; N . In the first works on pattern recognition, the task was to construct an informativeness criterion that maps N-dimensional to a space of lower dimension so that a solution can be made in this space. One of the advantages of this criterion is that the classification in a space of lower dimension is faster and easier, and the criterion of being constructed once.
Segmentation Techniques for Complex Image: Review

Segmentation is the technique where an image is partitioned into various regions comprising of similar attributes like intensity, texture, graysclae, etc represented by pixel patterns. Image segmentation has been generating research opportunities in this field for a long period. Various segmentation techniques like Otsu Thresholding, K-means clustering, Watershed Technique, Active Contours and Particle Swarm Optimization have been reviewed under this study. Survey of the mathematical equations along with the carrying-out of above segmentation techniques practically is the desired purpose of this study. The performance is analysed by estimation of the performance measurements i.e, TP, TN, FP and FN in numerical values.
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation

Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured functions and propose an additive tree-structured covariance function, showing improved sample-efficiency, wider applicability and greater flexibility. Furthermore, by incorporating the structure information of parameter spaces and the additive assumption in the BO loop, we develop a parallel algorithm to optimize the acquisition function and this optimization can be performed in a low dimensional space. We demonstrate our method on an optimization benchmark function, on a neural network compression problem and on pruning pre-trained VGG16 and ResNet50 models. Experimental results show our approach significantly outperforms the current state of the art for conditional parameter optimization including SMAC, TPE and Jenatton et al. (2017).
ClothFace: Battery-Free User Interface Solution Embedded into Clothing and Everyday Surroundings

This paper introduces ClothFace, a passive ultrahigh frequency (UHF) radio frequency identification (RFID) -based user interface solution, which can be embedded into clothing and into our everyday surroundings. The user interface platform consists of RFID tags, each of which has a unique ID. All the tags are initially readable to an external RFID reader. A specific tag can be switched off by covering it with a hand, which change can then be used as a digital input to any connected device. Because of the used passive UHF RFID technology, there is no need for embedded energy sources, but the interface platform gets all the needed energy from the external RFID reader. In this study, two test setups were created to an office environment: For the Body Test, the interface was integrated into a cotton shirt and into an item. For the Table Test, the interface was integrated into a wooden table. A gamelike testing software was created for both setups and two male test subjects tested the platform. The achieved results were very promising: success rates of 99-100 % and 94-98 % were reached in the Body Test and in the Table Test, respectively. Based on these promising preliminary results, we can envision the employment of ClothFace for developing multi-modal interfaces that can provide on-body gestural controls in body-based serious game applications.