Reviewerly/exHarmony huggingface dataset:
Large-Scale Feedforward Neural Network Optimization by a Self-Adaptive Strategy and Parameter Based Particle Swarm Optimization

Feedforward neural network (FNN) is one of the most widely used and fastest-developed artificial neural networks.Much evolutionary computation (EC) methods have been used to optimize the weights of FNN.However, as the dimension of datasets increases, the number of weights also increases dramatically.On high-dimensional datasets, if EC methods are used directly to optimize the weights of FNN, it is impossible to obtain the optimal weights of the FNN by EC methods in an acceptable time.Feature selection is a method that can effectively reduce the computational complexity of FNN by reducing irrelevant and redundant features.It may be practical to optimize the FNN by EC methods if we first employ the feature selection for the large-scale datasets.In this paper, we present a self-adaptive parameter and strategy-based particle swarm optimization (SPS-PSO) algorithm to optimize FNN with feature selection.First, we propose an optimization model for FNN by transforming the designing of FNN into a weights optimization problem.Simultaneously, we present a feature selection optimization model.Second, we present an SPS-PSO algorithm.In this algorithm, we use the parameter and strategy self-adaptive mechanism.In addition, five candidate solution generating strategies (CSGS) are used.The experiments are divided into two groups.In the first group, SPS-PSO and three other EC methods are used to directly optimize the weights of FNN on eight datasets without any modification.In the second group, we first employ SPS-PSO-based feature selection on the original datasets and obtain eight relatively smaller datasets with the k-nearest neighbor (KNN) which is used as the evaluation function for saving time.Then, we use the new datasets as the inputs for FNN.We optimize the weights of FNN again by SPS-PSO and three other EC methods to investigate whether we can get similar or even better classification accuracy by comparing the results with that of the first group.The experimental results show that SPS-PSO has the advantage in optimizing the weights of FNN compared with the other EC methods.Meanwhile, the SPS-PSO-based feature selection can reduce the solution size and computational complexity while ensuring the classification accuracy when it is used to preprocess the datasets for FNN.In this method, a solution with an originally higher than 700 000 dimensions can be even reduced to hundreds of dimensions.
Review of the Augmented Reality Systems for Shoulder Rehabilitation

Literature shows an increasing interest for the development of augmented reality (AR) applications in several fields, including rehabilitation. Current studies show the need for new rehabilitation tools for upper extremity, since traditional interventions are less effective than in other body regions. This review aims at: Studying to what extent AR applications are used in shoulder rehabilitation, examining wearable/non-wearable technologies employed, and investigating the evidence supporting AR effectiveness. Nine AR systems were identified and analyzed in terms of: Tracking methods, visualization technologies, integrated feedback, rehabilitation setting, and clinical evaluation. Our findings show that all these systems utilize vision-based registration, mainly with wearable marker-based tracking, and spatial displays. No system uses head-mounted displays, and only one system (11%) integrates a wearable interface (for tactile feedback). Three systems (33%) provide only visual feedback; 66% present visual-audio feedback, and only 33% of these provide visual-audio feedback, 22% visual-audio with biofeedback, and 11% visual-audio with haptic feedback. Moreover, several systems (44%) are designed primarily for home settings. Three systems (33%) have been successfully evaluated in clinical trials with more than 10 patients, showing advantages over traditional rehabilitation methods. Further clinical studies are needed to generalize the obtained findings, supporting the effectiveness of the AR applications.
Towards a reduction in architectural knowledge vaporization during agile global software development

The adoption of agile methods is a trend in global software development (GSD), but may result in many challenges. One important challenge is architectural knowledge (AK) management, since agile developers prefer sharing knowledge through face-to-face interactions, while in GSD the preferred manner is documents. Agile knowledge-sharing practices tend to predominate in GSD companies that practice agile development (AGSD), leading to a lack of documents, such as architectural designs, data models, deployment specifications, etc., resulting in the loss of AK over time, i.e., it vaporizes. In a previous study, we found that there is important AK in the log files of unstructured textual electronic media (UTEM), such as instant messengers, emails, forums, etc., which are the preferred means employed in AGSD to contact remote teammates. The objective of this paper is to present and evaluate a proposal with which to recover AK from UTEM logs. We developed and evaluated a prototype that implements our proposal in order to determine its feasibility. The evaluation was performed by conducting a study with agile/global developers and students, who used the prototype and different UTEM to execute tasks that emulate common situations concerning AGSD teams' lack of documentation during development phases. Our prototype was considered a useful, usable and unobtrusive tool when retrieving AK from UTEM logs. The participants also preferred our prototype when searching for AK and found AK faster with the prototype than with UTEM when the origin of the AK required was unknown. The participants' performance and perceptions when using our prototype provided evidence that our proposal could reduce AK vaporization in AGSD environments. These results encourage us to evaluate our proposal in a long-term test as future work.
Towards an Effective Digital Literacy Intervention to Assist Returning Citizens with Job Search

Returning citizens (formerly incarcerated individuals) face great challenges finding employment, and these are exacerbated by the need for digital literacy in modern job search. Through 23 semi-structured interviews and a pilot digital literacy course with returning citizens in the Greater Detroit area, we explore tactics and needs with respect to job search and digital technology. Returning citizens exhibit great diversity, but overall, we find our participants to have striking gaps in digital literacy upon release, even as they are quickly introduced to smartphones by friends and family. They tend to have employable skills and ability to use offline social networks to find opportunities, but have little understanding of formal job search processes, online or offline. They mostly mirror mainstream use of mobile technology, but they have various reasons to avoid social media. These and other findings lead to recommendations for digital literacy programs for returning citizens.
Joint Multi-View Hashing for Large-Scale Near-Duplicate Video Retrieval

Multi-view hashing can well support large-scale near-duplicate video retrieval, due to its desirable advantages of mutual reinforcement of multiple features, low storage cost, and fast retrieval speed. However, there are still two limitations that impede its performance. First, existing methods only consider local structures in multiple features. They ignore the global structure that is important for near-duplicate video retrieval, and cannot fully exploit the dependence and complementarity of multiple features. Second, existing works always learn hashing functions bit by bit, which unfortunately increases the time complexity of hash function learning. In this paper, we propose a supervised hashing scheme, termed as joint multi-view hashing (JMVH), to address the aforementioned problems. It jointly preserves the global and local structures of multiple features while learning hashing functions efficiently. Specially, JMVH considers features of video as items, based on which an underlying Hamming space is learned by simultaneously preserving their local and global structures. In addition, a simple but efficient multi-bit hash function learning based on generalized eigenvalue decomposition is devised to learn multiple hash functions within a single step. It can significantly reduce the time complexity of conventional hash function learning processes that sequentially learn multiple hash functions bit by bit. The proposed JMVH is evaluated on two public databases: CC_WEB_VIDEO and UQ_VIDEO. Experimental results demonstrate that the proposed JMVH achieves more than a 5 percent improvement compared to several state-of-the-art methods which indicates the superior performance of JMVH.
A New Robust Kalman Filter for SINS/DVL Integrated Navigation System

A new robust strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation algorithm are proposed in this paper with a focus on suppressing the process uncertainty and measurement outliers induced by severe manoeuvering. In the proposed algorithm, the one-step predicted probability density function is modeled as Student's t-distribution to deal with the heavy-tailed process noise, and hierarchical Gaussian state-space model for SINS/DVL integrated navigation algorithm is constructed. To detect and eliminate the measurement outliers, each measurement is marked by a binary indicator variable modeled as a beta-Bernoulli distribution. The variational Bayesian approach is used to jointly estimate state vector, auxiliary random variable, scale matrix, Bernoulli variable, and beta variable. The experimental results illustrate that the proposed algorithm has better robustness and navigation accuracy to deal with process uncertainty and measurement outliers than existing state-of-the-art algorithms.
Visualizing Uncertainty and Alternatives in Event Sequence Predictions

Data analysts apply machine learning and statistical methods to timestamped event sequences to tackle various problems but face unique challenges when interpreting the results. Especially in event sequence prediction, it is difficult to convey uncertainty and possible alternative paths or outcomes. In this work, informed by interviews with five machine learning practitioners, we iteratively designed a novel visualization for exploring event sequence predictions of multiple records where users are able to review the most probable predictions and possible alternatives alongside uncertainty information. Through a controlled study with 18 participants, we found that users are more confident in making decisions when alternative predictions are displayed and they consider the alternatives more when deciding between two options with similar top predictions.
Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields

As one of the various data mining areas, erasable pattern mining is a method for solving financial problems that can be caused in various industrial fields. Since the concept of erasable pattern mining was proposed, various relevant techniques have been developed. Dealing with dynamic stream data is an important issue in different data mining areas including erasable pattern mining. In order to deal with partial data with the latest information, a sliding window-based approach was proposed. However, the method still has performance limits in many cases because of its inefficient data structures and mining techniques. Motivated by this problem, we propose a new efficient erasable pattern mining algorithm based on the sliding window that can guarantee robustness and reliability and deal with the latest stream data more effectively. In addition, the newly proposed list data structure and mining techniques allow the proposed method to extract erasable patterns in a more efficient way. Results of empirical experiments using well-known benchmark datasets guarantee that the proposed algorithm outperforms previous state-of-the-art approaches in various aspects.
Reacting to the scope of a data breach: The differential role of fear and anger

We investigate how fearful and angry consumers react to the scope (number of customers affected) of a data breach. In two laboratory studies, we show that whereas fear makes consumers scope sensitive such that their intentions not to purchase from the affected retailer increases with scope, anger makes consumers scope insensitive and their repurchase intentions scope invariant. Process tests show that whereas scope indirectly affects the repurchase intentions of fearful consumers by making the mental image of the breach more vivid to them, scope does not affect how angry consumers imagine the breach nor their repurchase intentions. We find similar results in a field study, analyzing approximately 12,000 news stories of data breaches, showing that scope affects stock market reactions when the stories stress fear over anger but not when they stress anger over fear.
Few-Shot Transfer Learning for Text Classification With Lightweight Word Embedding Based Models

Many deep learning architectures have been employed to model the semantic compositionality for text sequences, requiring a huge amount of supervised data for parameters training, making it unfeasible in situations where numerous annotated samples are not available or even do not exist. Different from data-hungry deep models, lightweight word embedding-based models could represent text sequences in a plug-and-play way due to their parameter-free property. In this paper, a modified hierarchical pooling strategy over pre-trained word embeddings is proposed for text classification in a few-shot transfer learning way. The model leverages and transfers knowledge obtained from some source domains to recognize and classify the unseen text sequences with just a handful of support examples in the target problem domain. The extensive experiments on five datasets including both English and Chinese text demonstrate that the simple word embedding-based models (SWEMs) with parameter-free pooling operations are able to abstract and represent the semantic text. The proposed modified hierarchical pooling method exhibits significant classification performance in the few-shot transfer learning tasks compared with other alternative methods.
An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances

With the rapid deployment of the Internet of Things and cloud computing, it is necessary to enhance authentication protocols to reduce attacks and security vulnerabilities which affect the correct performance of applications. In 2019 a new lightweight IoT-based authentication scheme in cloud computing circumstances was proposed. According to the authors, their protocol is secure and resists very well-known attacks. However, when we evaluated the protocol we found some security vulnerabilities and drawbacks, making the scheme insecure. Therefore, we propose a new version considering login, mutual authentication and key agreement phases to enhance the security. Moreover, we include a sub-phase called evidence of connection attempt which provides proof about the participation of the user and the server. The new scheme achieves the security requirements and resists very well-known attacks, improving previous works. In addition, the performance evaluation demonstrates that the new scheme requires less communication-cost than previous authentication protocols during the registration and login phases.
The applicability of spectroscopy methods for estimating potentially toxic elements in soils: state-of-the-art and future trends

Potentially toxic elements (PTEs) in soils pose severe threats to the environment and human health.It is therefore imperative to have access to simple, rapid, portable, and accurate methods for their detection in soils.In this regard, the review introduces recent progresses made in the development and applications of spectroscopic methods for in situ semi-quantitative and quantitative detection of PTEs in soil and critically compares them to standard analytical methods.The advantages and limitations of these methods are discussed together with recent advances in chemometrics and data mining techniques allowing to extract useful information based on spectral data.Furthermore, the factors influencing soil spectra and data analysis are discussed and recommendations on how to reduce or eliminate their influences are provided.Future research and development needs for spectroscopy techniques are emphasized, and an analytical framework based on technology integration and data fusion is proposed to improve the measurement accuracy of PTEs in soil.
End-to-End Mandarin Speech Recognition Combining CNN and BLSTM

Since conventional Automatic Speech Recognition (ASR) systems often contain many modules and use varieties of expertise, it is hard to build and train such models. Recent research show that end-to-end ASRs can significantly simplify the speech recognition pipelines and achieve competitive performance with conventional systems. However, most end-to-end ASR systems are neither reproducible nor comparable because they use specific language models and in-house training databases which are not freely available. This is especially common for Mandarin speech recognition. In this paper, we propose a CNN+BLSTM+CTC end-to-end Mandarin ASR. This CNN+BLSTM+CTC ASR uses Convolutional Neural Net (CNN) to learn local speech features, uses Bidirectional Long-Short Time Memory (BLSTM) to learn history and future contextual information, and uses Connectionist Temporal Classification (CTC) for decoding. Our model is completely trained on the by-far-largest open-source Mandarin speech corpus AISHELL-1, using neither any in-house databases nor external language models. Experiments show that our CNN+BLSTM+CTC model achieves a WER of 19.2%, outperforming the exiting best work. Because all the data corpora we used are freely available, our model is reproducible and comparable, providing a new baseline for further Mandarin ASR research.
Experimental determination of the notch stress intensity factor for sharp V-notched specimens by using the digital image correlation method

Digital image correlation (DIC) method is used to experimentally determine the notch stress intensity factor for sharp V-notched samples under mode I loading conditions. First, two well-known test specimens, namely the single edge notch tension (SENT) and the single edge notch bending (SENB) specimens are chosen. The specimens are made of Polymethyl-methacrylate (PMMA) and for each specimen, five different notch opening angles are considered. Then, the experimental displacement fields in the specimens are derived by using DIC method. Afterward, the over-deterministic method is employed to derive the mode I notch stress intensity factors for the samples tested. According to the over-deterministic method, the analytical displacement field is fitted to a large amount of experimentally obtained local displacement values. Finally, the results obtained from DIC are compared with the results available in the literature. The good accuracy of the results obtained demonstrates the ability of DIC method in derivation of the stress field for sharp V-notched structures.
Mining the displacement of max-pooling for text recognition

The max-pooling operation in convolutional neural networks (CNNs) downsamples the feature maps of convolutional layers. However, in doing so, it loses some spatial information. In this paper, we extract a novel feature from pooling layers, called displacement features, and combine them with the features resulting from max-pooling to capture the structural deformations for text recognition tasks. The displacement features record the location of the maximal value in a max-pooling operation. Furthermore, we analyze and mine the class-wise trends of the displacement features. The extensive experimental results and discussions demonstrate that the proposed displacement features can improve the performance of the CNN based architectures and tackle the issues with the structural deformations of max-pooling in the text recognition tasks.
Semantic interpretability in hierarchical fuzzy systems: Creating semantically decouplable hierarchies

Analysing the interpretability of a fuzzy system (either hierarchical or not) involves consideration of its semantic properties and evaluation of its structural complexity. The present paper concentrates on the semantic aspects of interpretability in hierarchical fuzzy systems. Complexity reduction is also considered, but from the perspective of its interaction with semantic preservation. In that sense, the paper shows that only the use of intermediate variables with meaning (interpretable variables) will transform the complexity reduction into a real improvement in interpretability. The paper formalises this idea by introducing the concept of semantically decouplable hierarchies. Under the assumption of semantic decouplability, it is shown that the interpretability of the overall hierarchical system can be directly obtained from that of its subsystems. Consequently, the paper defines and measures the interpretability of a semantically decouplable hierarchical fuzzy system as the aggregation of the interpretability of its subsystems. Finally, several options will be considered for this aggregation process.
Learning Semantics-Preserving Attention and Contextual Interaction for Group Activity Recognition

In this paper, we investigate the problem of group activity recognition by learning semantics-perserving attention and contextual interaction among different people. Conventional methods usually aggregate the features extracted from individual persons by pooling operations, which lack physical meaning and cannot fully explore the contextual information for group activity recognition. To address this, we develop a Semantics-Preserving Teacher-Student (SPTS) networks architecture. Our SPTS networks first learn a Teacher Network in the semantic domain that classifies the word of group activity based on the words of individual actions. Then we design a Student Network in the appearance domain that recognizes the group activity according to the input video. We enforce the Student Network to mimic the Teacher Network in the learning procedure. In this way, we allocate semantics-preserving attention to different people, which is more effective to seek the key people and discard the misleading people, while no extra labelled data are required. Moreover, a group of people inherently lie in a graphbased structure, where the people and their relationship can be regarded as the nodes and edges of a graph respectively. Based on this, we build two graph convolutional modules on both the Teacher Network and the Student Network to reason the dependency among different people. Furthermore, we extend our approach on action segmentation task based on its intermediate features. Experimental results on four datasets for group activity analysis clearly show the superior performance of our method in comparisons with the state-of-the-arts.