Reviewerly/exHarmony huggingface dataset:
Ensuring Privacy of Data and Mined Results of Data Possessor in Collaborative ARM

The usage of the data mining (DM) technique has rapidly increased in the recent era. Most organizations utilize DM for forecasting their goals and for predicting various possibilities of solutions to their problems. DM provides various favors to our society; it also has some downsides like a risk to privacy and data security in collaborative mining. Privacy cracks occur eventually in the communication of data and aggregation of data. In the recent era, various approaches and methods for data privacy were obtained to achieve privacy of individual’s data and collaborative DM results, but yield into loss of information and undesirable effect on the utility of data; as a result, DM success is downgraded. In this paper, we proposed an effectual approach—Fisher–Yates shuffle algorithm for privacy-preserving (PP) association rule mining (ARM). With our approach, medical supervision can steadily discover a global verdict model through their local verdict models without the aid of cloud, and the perceptive medical data of each medical supervision is well protected. Hence, association among some delicate diseases like coronavirus and its symptoms, treatment, and remedy helps in foreseeing the disease in the beginning time. Our target is to conclude association rules in a dispersed environment with reasonably reduced communication time and computation costs, preserves the privacy of participants, and gives precise results.
Learning efficient navigation in vortical flow fields

Abstract Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying. In such applications, robots may only have knowledge of their immediate surroundings or be faced with time-varying currents, which limits the use of optimal control techniques. Here, we apply a recently introduced Reinforcement Learning algorithm to discover time-efficient navigation policies to steer a fixed-speed swimmer through unsteady two-dimensional flow fields. The algorithm entails inputting environmental cues into a deep neural network that determines the swimmer’s actions, and deploying Remember and Forget Experience Replay. We find that the resulting swimmers successfully exploit the background flow to reach the target, but that this success depends on the sensed environmental cue. Surprisingly, a velocity sensing approach significantly outperformed a bio-mimetic vorticity sensing approach, and achieved a near 100% success rate in reaching the target locations while approaching the time-efficiency of optimal navigation trajectories.
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection

Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sources complementary and beneficial to each other. In this paper, we propose AutoAlign, an automatic feature fusion strategy for 3D object detection. Instead of establishing deterministic correspondence with camera projection matrix, we model the mapping relationship between the image and point clouds with a learnable alignment map. This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner. Specifically, a cross-attention feature alignment module is devised to adaptively aggregate pixel-level image features for each voxel. To enhance the semantic consistency during feature alignment, we also design a self-supervised cross-modal feature interaction module, through which the model can learn feature aggregation with instance-level feature guidance. Extensive experimental results show that our approach can lead to 2.3 mAP and 7.0 mAP improvements on the KITTI and nuScenes datasets respectively. Notably, our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving competitive performance among various state-of-the-arts.
Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes

Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents’ lives with minimal cost, daily activity recognition aims to know resident’s daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning (ML) techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition.
Research on the Internet of Things Device Recognition Based on RF-Fingerprinting

Internet of Things (IoT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IoT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application of its system. The radio frequency fingerprinting (RFF) is a unique characteristic closely related to the hardware of IoT devices themselves, which is difficultly tampered. In this paper, four kinds of RF fingerprint feature extraction algorithms based on statistical features are studied. Robust principle component analysis (RPCA) is used for the dimensionality reduction and the support vector machines (SVM) is used for classification. Through theoretical modeling and experimental verification, the reliability and distinguishability of RFFs are extracted and evaluated, and the classification results are displayed in the real IoT equipment environment.
Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371)

The increasingly pervasive nature of the expanding to devices and things in everydaylife, along with new trends in Artificial Intelligence call for new paradigms and a new look onKnowledge Representation and Processing at scale for the Semantic Web. The emerging, but stillto be concretely shaped concept of provides an excellent unifying metaphorfor this current status of Semantic Web research. More than two decades of Semantic Webresearch provides a solid basis and a promising technology and standards stack to interlink data,ontologies and knowledge on the Web. However, neither are applications for Knowledge Graphsas such limited to Linked Open Data, nor are instantiations of Knowledge Graphs in enterprises– while often inspired by – limited to the core Semantic Web stack. This report documents theprogram and the outcomes of Dagstuhl Seminar 18371 Graphs: New Directions forKnowledge Representation on the Semantic Web, where a group of experts from academia andindustry discussed fundamental questions around these topics for a week in early September 2018,including the following: what are knowledge graphs? Which applications do we see to emerge?Which open research questions still need be addressed and which technology gaps still need tobe closed?
Automatic basis-set adaptation in projection-based embedding

Projection-based embedding (PbE) is an exact embedding method within density-functional theory (DFT) that has received increasing attention in recent years. Several different variants have been described in the literature, but no systematic comparison has been presented so far. The truncation of the basis is critical for the efficiency of this class of approaches. Here, we employ a basis-set truncation scheme previously used for level-shift embedding in a top-down fashion, and we present an own basis-set extension scheme for bottom-up type PbE. We compare its accuracy for the level-shift technique [Manby et al., J. Chem. Theory Comput. 8, 2564-2568 (2012)] and an empirically corrected variant, the external-orthogonality approach by Khait and Hoffmann [Annu. Rep. Comput. Chem. 8, 53-70 (2012)] and the approach based on the Huzinaga equation transferred to the DFT context [Hégely et al., J. Chem. Phys. 145, 064107 (2016)]. Concerning the reproduction in total energies, we show that the Huzinaga method yields the most stable results concerning a basis-set truncation in top-down embedding. For the practically more relevant calculation of energy differences, the efficient level-shift technique yields very promising results due to error cancellation. In bottom-up embedding, we observe convergence issues in cases where constraints in the Lagrange formalism cannot be fulfilled due to basis-set incompleteness.
Blockchain in health care: hype, trust, and digital health

The promise of digital health care continues to be enthusiastically promoted but has been difficult to realise. Evidence-based practice, along with patient safety assurances, information governance, and certification, does not fit easily with schemes to accelerate innovation. Trust in the efficacy and safety of new models of care, such as that potentially delivered by artificial intelligence (AI), remains uncertain. Blockchain technology might contribute to overcoming these problems by enabling greater openness, transparency, and trust.
Recurring concept meta-learning for evolving data streams

When concept drift is detected during classification in a data stream, a common remedy is to retrain a framework’s classifier. However, this loses useful information if the classifier has learnt the current concept well, and this concept will recur again in the future. Some frameworks retain and reuse classifiers, but it can be time-consuming to select an appropriate classifier to reuse. These frameworks rarely match the accuracy of state-of-the-art ensemble approaches. For many data stream tasks, speed is important: fast, accurate frameworks are needed for time-dependent applications. We propose the Enhanced Concept Profiling Framework (ECPF), which aims to recognise recurring concepts and reuse a classifier trained previously, enabling accurate classification immediately following a drift. The novelty of ECPF is in how it uses similarity of classifications on new data, between a new classifier and existing classifiers, to quickly identify the best classifier to reuse. It always trains both a new classifier and a reused classifier, and retains the more accurate classifier when concept drift occurs. Finally, it creates a copy of reused classifiers, so a classifier well-suited for a recurring concept will not be impacted by being trained on a different concept. In our experiments, ECPF classifies significantly more accurately than a state-of-the-art classifier reuse framework (Diversity Pool) and a state-of-the-art ensemble technique (Adaptive Random Forest) on synthetic datasets with recurring concepts. It classifies real-world datasets five times faster than Diversity Pool, and six times faster than Adaptive Random Forest and is not significantly less accurate than either.
On $\sigma$ -LCD Codes

Linear complementary pairs (LCPs) of codes play an important role in armoring implementations against sidechannel attacks and fault injection attacks. One of the most common ways to construct LCP of codes is to use Euclidean linear complementary dual (LCD) codes. In this paper, we first introduce the concept of linear codes with o complementary dual (σ-LCD), which includes known Euclidean LCD codes, Hermitian LCD codes, and Galois LCD codes. Like Euclidean LCD codes, σ-LCD codes can also be used to construct LCP of codes. We show that for q 2, all q-ary linear codes are σ-LCD, and for every binary linear code C, the code {0} × C is σ-LCD. Furthermore, we study deeply σ-LCD generalized quasi-cyclic (GQC) codes. In particular, we provide the characterizations of σ-LCD GQC codes, self-orthogonal GQC codes, and self-dual GQC codes, respectively. Moreover, we provide the constructions of asymptotically good σ-LCD GQC codes. Finally, we focus on σ-LCD abelian codes and prove that all abelian codes in a semisimple group algebra are σ-LCD. The results derived in this paper extend those on the classical LCD codes and show that σ-LCD codes allow the construction of LCP of codes more easily and with more flexibility.
Demonstration of Federated Learning in a Resource-Constrained Networked Environment

Many modern applications in the area of smart computing are based on machine learning techniques. To train machine learning models, a large amount of data is usually required, which is often not readily available at a central location. Federated learning enables the training of machine learning models from distributed datasets at client devices without transmitting the data to a central place, which has benefits including preserving the privacy of user data and reducing communication bandwidth. In this demonstration, we show a federated learning system deployed in an emulated wide-area communications network with dynamic, heterogeneous, and intermittent resource availability, where the network is emulated using a CORE/EMANE emulator. In our system, the environment is decentralized and each client can ask for assistance by other clients. The availability of clients is intermittent so only those clients that are available can provide assistance. A graphical interface illustrates the network connections and the user can adjust these connections through the interface. A user interface displays the training progress and each client's contribution to training.
ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation

Session-based recommendation is a challenging problem due to the inherent uncertainty of user behavior and the limited historical click information. Latent factors and the complex dependencies within the user’s current session have an important impact on the user's main intention, but the existing methods do not explicitly consider this point. In this paper, we propose a novel model, Interest Shift and Latent Factors Combination Model (ISLF), which can capture the user's main intention by taking into account the user’s interest shift (i.e. long-term and short-term interest) and latent factors simultaneously. In addition, we experimentally give an explicit explanation of this combination in our ISLF. Our experimental results on three benchmark datasets show that our model achieves state-of-the-art performance on all test datasets.
Can SGD Learn Recurrent Neural Networks with Provable Generalization

Recurrent Neural Networks (RNNs) are among the most popular models in sequential data analysis. Yet, in the foundational PAC learning language, what concept class can it learn? Moreover, how can the same recurrent unit simultaneously learn functions from different input tokens to different output tokens, without affecting each other? Existing generalization bounds for RNN scale exponentially with the input length, significantly limiting their practical implications. In this paper, we show using the vanilla stochastic gradient descent (SGD), RNN can actually learn some notable concept class \emph{efficiently}, meaning that both time and sample complexity scale \emph{polynomially} in the input length (or almost polynomially, depending on the concept). This concept class at least includes functions where each output token is generated from inputs of earlier tokens using a smooth two-layer neural network.
Using Augmented Reality in Patients with Autism: A Systematic Review

Augmented Reality (AR) technology has been used for a wide range of areas, uncovering diverse benefits regarding to its usage. Focusing on medicine, AR is being used for the last few decades in several different tasks – from surgery support to treatment of phobias and other cognitive or physiological disorders. This study intends to focus on the usage of AR in one specific pathology: the rehabilitation of people that suffer from autism. This systematic review will present an overview of the usage of AR technology, from a patients’ perspective, where strengths and weaknesses of current approaches will be pointed out, to better understand good practices to follow when using AR for autism rehabilitation. The results from this systematic review will provide valuable information for further ideas and solutions regarding to enhance Autism Spectrum Disorder patients’ wellbeing.
Honeycrisp

Recently, a number of systems have been deployed that gather sensitive statistics from user devices while giving differential privacy guarantees. One prominent example is the component in Apple's macOS and iOS devices that collects information about emoji usage and new words. However, these systems have been criticized for making unrealistic assumptions, e.g., by creating a very high "privacy budget" for answering queries, and by replenishing this budget every day, which results in a high worst-case privacy loss. However, it is not obvious whether such assumptions can be avoided if one requires a strong threat model and wishes to collect data periodically, instead of just once.
Fine-Grained Propaganda Detection with Fine-Tuned BERT

This paper presents the winning solution of the Fragment Level Classification (FLC) task in the Fine Grained Propaganda Detection competition at the NLP4IF'19 workshop.The goal of the FLC task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset.The main idea of our solution is to perform word-level classification using finetuned BERT, a popular pre-trained language model.Besides presenting the model and its evaluation results, we also investigate the attention heads in the model, which provide insights into what the model learns, as well as aspects for potential improvements. 1
Blockchain technology in the pharmaceutical supply chain: researching a business model based on Hyperledger Fabric

Pharmaceutical companies that manufacture, ship and supply products face difficulties in tracking their products, allowing counterfeiters to inject counterfeit drugs into the system. This situation is observed, in particular, in the Russian market of turnover of pharmaceutical products, although the problem has long been global. The ability of blockchain systems to pinpoint the origin of data makes them particularly suitable for pharmaceutical supply chain applications. The data stored in the blockchain distributed register on the identification of drugs produced by the plant, as well as records of their movements throughout the supply chain, can accurately determine the authenticity of pharmaceutical products lying on the shelves of pharmacies. The development and implementation of such a system can be a big step towards winning the exhausting fight against the easy availability of counterfeit drugs and medical products. In the first part of this work, the main characteristics and features of the functioning of blockchain systems will be studied. In the second and final part, the designed concept of the pharmaceutical turnover control system based on the blockchain technology Hyperledger Fabric using the Hyperledger Composer development environment will be investigated.
Arabic Text Classification of News Articles Using Classical Supervised Classifiers

Automatic document categorization gains more importance in view of the plethora of textual documents added constantly on the web. Text categorization or classification is the process of automatically tagging a textual document with most relevant label. Text categorization for Arabic language is interesting in the absence of large and free datasets. Our objective is to automatically identify the category of a document based on its linguistic features. To achieve this goal, we constructed a new dataset which contains almost 90k Arabic news articles with their tags from Arabic news portals. The dataset shall be made freely available to the research community on Arabic computational linguistics. The dataset has four main categories: Business, Sports, Technology and Middle East. Each collected article was cleaned from Latin characters, numbers, punctuation and stop words. To investigate the effectiveness of the dataset, we used an array of classical supervised machine learning classifiers. Namely, the following 10 popular classifiers were used: Logistic Regression, Nearest Centroid, Decision Tree (DT), Support Vector Machines (SVM), K-nearest neighbors (KNN), XGBoost Classifier, Random Forest Classifier, Multinomial Classifier, Ada-Boost Classifier, and Multi-Layer Perceptron (MLP). In pursuit of high accuracy, we implemented an ensemble model to combine best classifiers together in a majority-voting classifier. Our experimental results showed solid performance with a minimum Fl-score of 87.7%, achieved by Ada-Boost and top performance of 97.9% achieved by SVM. The experimental results are presented in terms of confusion matrices, Fl-scores, and accuracy.
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance. Previous approaches mainly rely on probabilistic models or predefined rules for modeling co-occurrence relationships among AUs, leading to limited generalization. In contrast, we present a new learning framework that automatically learns the latent relationships of AUs via establishing semantic correspondences between feature maps. In the heatmap regression-based network, feature maps preserve rich semantic information associated with AU intensities and locations. Moreover, the AU co-occurring pattern can be reflected by activating a set of feature channels, where each channel encodes a specific visual pattern of AU. This motivates us to model the correlation among feature channels, which implicitly represents the co-occurrence relationship of AU intensity levels. Specifically, we introduce a semantic correspondence convolution (SCC) module to dynamically compute the correspondences from deep and low resolution feature maps, and thus enhancing the discriminability of features. The experimental results demonstrate the effectiveness and the superior performance of our method on two benchmark datasets.
Top-Down RST Parsing Utilizing Granularity Levels in Documents

Some downstream NLP tasks exploit discourse dependency trees converted from RST trees. To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures. Thus, we propose a novel neural top-down RST parsing method. Then, we exploit three levels of granularity in a document, paragraphs, sentences and Elementary Discourse Units (EDUs), to parse a document accurately and efficiently. The parsing is done in a top-down manner for each granularity level, by recursively splitting a larger text span into two smaller ones while predicting nuclearity and relation labels for the divided spans. The results on the RST-DT corpus show that our method achieved the state-of-the-art results, 87.0 unlabeled span score, 74.6 nuclearity labeled span score, and the comparable result with the state-of-the-art, 60.0 relation labeled span score. Furthermore, discourse dependency trees converted from our RST trees also achieved the state-of-the-art results, 64.9 unlabeled attachment score and 48.5 labeled attachment score.