Reviewerly/exHarmony huggingface dataset:
Random Search One Dimensional CNN for Human Activity Recognition

Due to its wide application, human activity recognition (HAR) has become a common subject for research specially with the development of deep learning. Many researchers believe that deep convolutional neural networks (DCNN) are ideal for feature extraction from signal inputs. This has gained widespread interest in using these methods to identify human actions on the mobile phone in real time. A deep network architecture using random search one dimensional convolutional neural network (RS-1D-CNN) is proposed to find best networks connections and hyper-parameters to enhance model performance. Batch normalization (BN) layer was added to speed up the convergence. Moreover, we have applied a global average pooling (GAP) for dimensionality reduction and to reduce model hyper-parameters, followed two dense connected layers. The final dense layer has a softmax activation function and a node for each potential object category. Public UCI-HAR dataset was used to evaluate model performance. Random search has been utilized to perform hyper parameter tuning to determine the optimal model parameters. Proposed model will automatically extract and classify human behaviours. Daily human activities that provided by UCI-HAR include (walking, jogging, sitting, standing, upstairs and downstairs). Results has shown that our approach outperforms both CNN, LSTM method and other state-of-the-art approaches.
Multiple adversarial networks for unsupervised domain adaptation

Domain adaptation algorithm is a powerful tool for transferring the knowledge of source domain with sufficient annotations for target tasks. Recently, adversarial learning is embedded into deep networks to reduce domain shift between source and target domains for learning transferable features. Existing adversarial domain adaptation methods aim at reducing the source and target domain discrepancy ignoring the class discrepancy between source and target domains. This paper proposes a novel Multiple Adversarial Networks (MAN) for unsupervised domain adaptation. MAN utilizes a pair of classifiers to minimize inter-domain discrepancy and embeds a domain discriminator for each category for intra-class discrepancy. Furthermore, we extend our MAN as improved MAN (iMAN) by utilizing a feature norm term to regularize the task-specific features, which can improve model generalization and help for minimizing intra-class discrepancy. We conduct extensive experiments on two real-world datasets Office–Home and ImageCLEF-DA, and experiment results show the effectiveness and superiority of our methods compared with several state-of-the-art unsupervised domain adaptation methods.
The Impact of Weighting Schemes and Stemming Process on Topic Modeling of Arabic Long and Short Texts

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combination term weighting scheme, namely, CmTLB. We utilize the mTFIDF that takes into account the missing terms and the number of the documents in which the term appears when calculating the term weight. For further robust term weight, we combine mTFIDF with two weighting methods. We evaluate CmTLB against the studied weighting schemes by the quality of the learned topics (topic visualization and topic coherence), classification, and clustering tasks. We applied weighting schemes to Latent Dirichlet allocation (LDA) and Dirichlet multinomial mixture (DMM) on eight Arabic long and short document datasets, respectively. The experiment results outline that appropriate weighting schemes can effectively improve topic modeling performance on Arabic texts. More importantly, our proposed CmTLB significantly outperforms the other weighting schemes. Secondly, we investigate whether the Arabic stemming process can improve topic modeling performance. We study the three approaches of Arabic stemming including root-based, stem-based, and statistical approaches. We also train topic models with weighting schemes on documents after applying four stemmers related to different stemming approaches. The results outline that applying the stemming process not only reduces the dimensionality of term-document matrix leading to fast estimation process, but also show enhancement of topic modeling performance both on short and long Arabic documents. Moreover, Farasa stemmer achieves the highest performance in most cases, since it prevents the ambiguity that may happen because of the blind removal of the affixes such as in root-based or stem-based stemmers.
Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

Multi-robot cooperative patrolling systems have been extensively employed in the civilian and military fields, including monitoring forest fires, marine search-and-rescue, and area patrol. Multi-robot area patrol problems refer to the activity that a team of robots works cooperatively and regularly to visit the key targets in the given area for security. Following consideration of the low cost and high safety of unmanned surface vehicles (USV), a team of USVs is organized to perform area patrol in a sophisticated maritime environment. In this paper, we establish a mathematical model considering the characteristics of the cooperative patrol task and the limited conditions of USVs. A hybrid partition-based patrolling scheme is proposed for a multi-USV system to visit targets with different importance levels in a maritime area. Firstly, a centralized area partition algorithm is utilized to partition the patrolling area according to the number of USVs. Secondly, a distributed path planning algorithm is applied to planning the patrolling path for each USV to visit the targets in a maritime environment to minimize the length of the patrolling path for the USV team. Finally, comparative experiments between the proposed scheme and other methods are carried out to validate the performance of the hybrid partition-based patrolling scheme. Simulation results and experimental analysis show the efficiency of the proposed hybrid partition-based patrolling scheme compared to several previous patrolling algorithms.
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency

There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers. Inspired by the observation that humans are able to recognize objects that appear out of place in a scene or along with other unlikely objects, we augment the DNN with a system that learns context consistency rules during training and checks for the violations of the same during testing. Our approach builds a set of auto-encoders, one for each object class, appropriately trained so as to output a discrepancy between the input and output if an added adversarial perturbation violates context consistency rules. Experiments on PASCAL VOC and MS COCO show that our method effectively detects various adversarial attacks and achieves high ROC-AUC (over 0.95 in most cases); this corresponds to over 20% improvement over a state-of-the-art context-agnostic method.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB).However, the conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types, harboring inherently different characteristics, e.g., difficulty level.This paper proposes a metareinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.Our method quickly and effectively adapts the meta-learned programmer to new questions based on the most similar questions retrieved from the training data.The meta-learned policy is then used to learn a good programming policy, utilizing the trial trajectories and their rewards for similar questions in the support set.Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.We have released our code at
Multi-view Consensus CNN for 3D Facial Landmark Placement

The rapid increase in the availability of accurate 3D scanning devices has moved facial recognition and analysis into the 3D domain. 3D facial landmarks are often used as a simple measure of anatomy and it is crucial to have accurate algorithms for automatic landmark placement. The current state-of-the-art approaches have yet to gain from the dramatic increase in performance reported in human pose tracking and 2D facial landmark placement due to the use of deep convolutional neural networks (CNN). Development of deep learning approaches for 3D meshes has given rise to the new subfield called geometric deep learning, where one topic is the adaptation of meshes for the use of deep CNNs. In this work, we demonstrate how methods derived from geometric deep learning, namely multi-view CNNs, can be combined with recent advances in human pose tracking. The method finds 2D landmark estimates and propagates this information to 3D space, where a consensus method determines the accurate 3D face landmark position. We utilise the method on a standard 3D face dataset and show that it outperforms current methods by a large margin. Further, we demonstrate how models trained on 3D range scans can be used to accurately place anatomical landmarks in magnetic resonance images.
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering

Compositional visual question answering requires reasoning over both semantic and geometry object relations. We propose a novel tiered reasoning method that dynamically selects object level candidates based on language representations and generates robust pairwise relations within the selected candidate objects. The proposed tiered relation reasoning method can be compatible with the majority of the existing visual reasoning frameworks, leading to significant performance improvement with very little extra computational cost. Moreover, we propose a policy network that decides the appropriate reasoning steps based on question complexity and current reasoning status. In experiments, our model achieves state-of-the-art performance on two VQA datasets.
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing

Semantic segmentation for lightweight object parsing is a very challenging task, because both accuracy and efficiency (e.g., execution speed, memory footprint or computational complexity) should all be taken into account. However, most previous works pay too much attention to one-sided perspective, either accuracy or speed, and ignore others, which poses a great limitation to actual demands of intelligent devices. To tackle this dilemma, we propose a novel lightweight architecture named Context-Integrated and Feature-Refined Network (CIFReNet). The core components of CIFReNet are the Long-skip Refinement Module (LRM) and the Multi-scale Context Integration Module (MCIM). The LRM is designed to ease the propagation of spatial information between low-level and high-level stages. Furthermore, channel attention mechanism is introduced into the process of long-skip learning to boost the quality of low-level feature refinement. Meanwhile, the MCIM consists of three cascaded Dense Semantic Pyramid (DSP) blocks with image-level features, which is presented to encode multiple context information and enlarge the field of view. Specifically, the proposed DSP block exploits a dense feature sampling strategy to enhance the information representations without significantly increasing the computation cost. Comprehensive experiments are conducted on three benchmark datasets for object parsing including Cityscapes, CamVid, and Helen. As indicated, the proposed method reaches a better trade-off between accuracy and efficiency compared with the other state-of-the-art methods.
Twist-teleportation-based local discrimination of maximally entangled states

In this work, we study the local distinguishability of maximally entangled states (MESs). In particular, we are concerned with whether any fixed number of MESs can be locally distinguishable for sufficiently large dimensions. Fan and Tian \emph{et al.} have already obtained two satisfactory results for the generalized Bell states (GBSs) and the qudit lattice states when applied to prime or prime power dimensions. We construct a general twist-teleportation scheme for any orthonormal basis with MESs that is inspired by the method used in [Phys. Rev. A \textbf{70}, 022304 (2004)]. Using this teleportation scheme, we obtain a sufficient and necessary condition for one-way distinguishable sets of MESs, which include the GBSs and the qudit lattice states as special cases. Moreover, we present a generalized version of the results in [Phys. Rev. A \textbf{92}, 042320 (2015)] for the arbitrary dimensional case.
Secure Quantum Network Code Without Classical Communication

We consider the secure quantum communication over a network with the presence of a malicious adversary who can eavesdrop and contaminate the states. The network consists of noiseless quantum channels with the unit capacity and the nodes which applies noiseless quantum operations. As the main result, when the maximum number m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> of the attacked channels over the entire network uses is less than a half of the network transmission rate m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (i.e., m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> <; m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /2), our code implements secret and correctable quantum communication of the rate m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> - 2m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> by using the network asymptotic number of times. Our code is universal in the sense that the code is constructed without the knowledge of the specific node operations and the network topology, but instead, every node operation is constrained to the application of an invertible matrix to the basis states. Moreover, our code requires no classical communication. Our code can be thought of as a generalization of the quantum secret sharing.
Context-sensitive gender inference of named entities in text

The gender information of named entities is an important prerequisite for many text analysis tasks such as gender bias detection and targeted advertising. Despite its valuable use cases, gender tagging of named entities has traditionally been database-reliant. The lack of open-source benchmarks is a major impediment to exploring the effectiveness of machine learning-based methods for this task. Towards this goal, the article serves two main purposes. Firstly, we create four open-source datasets from well-known NER corpora and make them publicly available. Secondly, we propose a novel supervised learning approach based on the transformer network to identify the gender of named entities. We evaluate the proposed approach on four gender identification datasets. The proposed method outperforms two commercial database-reliant approaches and five deep sequence models, including BERT.
An Efficient Query Scheme for Hybrid Storage Blockchains Based on Merkle Semantic Trie

As a decentralized trusted database, the blockchain is finding applications in a growing number of fields such as finance, supply chain and medicine traceability, where large volumes of valuable data are stored on the blockchain. Currently, the mainstream blockchains employ a hybrid data storage architecture combining on-chain and off-chain storage. Real-time distributed search of mass data stored in this hybrid system is now a major need. However, previous fast retrieval schemes for the blockchain system are aimed only at on-chain data without considering their relevance to off-chain data, and thus fail to meet the requirement. In this paper, we propose an efficient blockchain data query scheme by introducing a novel Merkle Semantic Trie-based indexing technique without modifying the underlying database. A consensus on-chain index structure is constructed using the extracted semantic information of the off-chain data to create a mapping between the on-chain and off-chain data, thus enabling real-time data query both on and off the chain. Our scheme also provides multiple complex analytical query primitives to support semantic query, range query, and even fuzzy query. Experiments on three open data sets show that the proposed scheme has good query performance with shorter query latency for four different search types and offers better retrieval performance and verification efficiency than those available.
Example-Guided Image Synthesis Using Masked Spatial-Channel Attention and Self-supervision

Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplar image provides the style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the existing models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is a scene image that is semantically different from the given label map. To this end, we first propose a Masked Spatial-Channel Attention (MSCA) module which models the correspondence between two scenes via efficient decoupled attention. Next, we propose an end-to-end network for joint global and local feature alignment and synthesis. Finally, we propose a novel self-supervision task to enable training. Experiments on the large-scale and more diverse COCO-stuff dataset show significant improvements over the existing methods. Moreover, our approach provides interpretability and can be readily extended to other content manipulation tasks including style and spatial interpolation or extrapolation.
Study of Extractive Text Summarizer Using The Elmo Embedding

In recent times, data excessiveness has become a major problem in the field of education, news, blogs, social media, etc. Due to an increase in such a vast amount of text data, it became challenging for a human to extract only the valuable amount of data in a concise form. In other words, summarizing the text, enables human to retrieves the relevant and useful texts, Text summarizing is extracting the data from the document and generating the short or concise text of the document. One of the major approaches that are used widely is Automatic Text summarizer. Automatic text summarizer analyzes the large textual data and summarizes it into the short summaries containing valuable information of the data. Automatic text summarizer further divided into two types 1) Extractive text summarizer, 2) Abstractive Text summarizer. In this article, the extractive text summarizer approach is being looked for. Extractive text summarization is the approach in which model generates the concise summary of the text by picking up the most relevant sentences from the text document. This paper focuses on retrieving the valuable amount of data using the Elmo embedding in Extractive text summarization. Elmo embedding is a contextual embedding that had been used previously by many researchers in abstractive text summarization techniques, but this paper focus on using it in extractive text summarizer.
Automatic classification of participant roles in cyberbullying: Can we detect victims, bullies, and bystanders in social media text?

Abstract Successful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of the event nor the participants involved. Automatic analysis of participant roles in cyberbullying traces enables targeted bullying prevention strategies. In this paper, we aim to automatically detect different participant roles involved in textual cyberbullying traces, including bullies, victims, and bystanders. We describe the construction of two cyberbullying corpora (a Dutch and English corpus) that were both manually annotated with bullying types and participant roles and we perform a series of multiclass classification experiments to determine the feasibility of text-based cyberbullying participant role detection. The representative datasets present a data imbalance problem for which we investigate feature filtering and data resampling as skew mitigation techniques. We investigate the performance of feature-engineered single and ensemble classifier setups as well as transformer-based pretrained language models (PLMs). Cross-validation experiments revealed promising results for the detection of cyberbullying roles using PLM fine-tuning techniques, with the best classifier for English (RoBERTa) yielding a macro-averaged ${F_1}$ -score of 55.84%, and the best one for Dutch (RobBERT) yielding an ${F_1}$ -score of 56.73%. Experiment replication data and source code are available at https://osf.io/nb2r3 .
Quantum sensing of open systems: Estimation of damping constants and temperature

We determine quantum precision limits for estimation of damping constants and temperature of lossy bosonic channels. A direct application would be the use of light for estimation of the absorption and the temperature of a transparent slab. Analytic lower bounds are obtained for the uncertainty in the estimation, through a purification procedure that replaces the master equation description by a unitary evolution involving the system and ad hoc environments. For zero temperature, Fock states are shown to lead to the minimal uncertainty in the estimation of damping, with boson-counting being the best measurement procedure. In both damping and temperature estimates, sequential pre-thermalization measurements, through a stream of single bosons, may lead to huge gain in precision.
Financial Crime &amp; Fraud Detection Using Graph Computing: Application Considerations &amp; Outlook

In recent years, the unprecedented growth in digital payments fueled consequential changes in fraud and financial crimes. In this new landscape, traditional fraud detection approaches such as rule-based engines have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest. Graph neural networks and emerging adaptive solutions provide compelling opportunities for the future of fraud and financial crime detection. However, implementing the graph-based solutions in financial transaction processing systems has brought numerous obstacles and application considerations to light. In this paper, we overview the latest trends in the financial crimes landscape and discuss the implementation difficulties current and emerging graph solutions face. We argue that the application demands and implementation challenges provide key insights in developing effective solutions.