Reviewerly/exHarmony huggingface dataset:
IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43% with 39% fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49% with 31.8% fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5% with 0.38% fewer FLOPs.
A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning

The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.
Adversarial Attack on Deep Product Quantization Network for Image Retrieval

Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets. Recent studies show that deep neural networks (DNNs) are vulnerable to input with small and maliciously designed perturbations (a.k.a., adversarial examples). This phenomenon raises the concern of security issues for DPQN in the testing/deploying stage as well. However, little effort has been devoted to investigating how adversarial examples affect DPQN. To this end, we propose product quantization adversarial generation (PQ-AG), a simple yet effective method to generate adversarial examples for product quantization based retrieval systems. PQ-AG aims to generate imperceptible adversarial perturbations for query images to form adversarial queries, whose nearest neighbors from a targeted product quantizaiton model are not semantically related to those from the original queries. Extensive experiments show that our PQ-AQ successfully creates adversarial examples to mislead targeted product quantization retrieval models. Besides, we found that our PQ-AG significantly degrades retrieval performance in both white-box and black-box settings.
SalSAC: A Video Saliency Prediction Model with Shuffled Attentions and Correlation-Based ConvLSTM

The performance of predicting human fixations in videos has been much enhanced with the help of development of the convolutional neural networks (CNN). In this paper, we propose a novel end-to-end neural network “SalSAC” for video saliency prediction, which uses the CNN-LSTM-Attention as the basic architecture and utilizes the information from both static and dynamic aspects. To better represent the static information of each frame, we first extract multi-level features of same size from different layers of the encoder CNN and calculate the corresponding multi-level attentions, then we randomly shuffle these attention maps among levels and multiply them to the extracted multi-level features respectively. Through this way, we leverage the attention consistency across different layers to improve the robustness of the network. On the dynamic aspect, we propose a correlation-based ConvLSTM to appropriately balance the influence of the current and preceding frames to the prediction. Experimental results on the DHF1K, Hollywood2 and UCF-sports datasets show that SalSAC outperforms many existing state-of-the-art methods.
Capturing Greater Context for Question Generation

Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension. While questions are often asked with respect to long documents, there are many challenges with modeling such long documents. Many existing techniques generate questions by effectively looking at one sentence at a time, leading to questions that are easy and not reflective of the human process of question generation. Our goal is to incorporate interactions across multiple sentences to generate realistic questions for long documents. In order to link a broad document context to the target answer, we represent the relevant context via a multi-stage attention mechanism, which forms the foundation of a sequence to sequence model. We outperform state-of-the-art methods on question generation on three question-answering datasets - SQuAD, MS MARCO and NewsQA. 1
advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns

Person re-identification (re-ID) is the task of matching person images across camera views, which plays an important role in surveillance and security applications. Inspired by great progress of deep learning, deep re-ID models began to be popular and gained state-of-the-art performance. However, recent works found that deep neural networks (DNNs) are vulnerable to adversarial examples, posing potential threats to DNNs based applications. This phenomenon throws a serious question about whether deep re-ID based systems are vulnerable to adversarial attacks. In this paper, we take the first attempt to implement robust physical-world attacks against deep re-ID. We propose a novel attack algorithm, called advPattern, for generating adversarial patterns on clothes, which learns the variations of image pairs across cameras to pull closer the image features from the same camera, while pushing features from different cameras farther. By wearing our crafted “invisible cloak”, an adversary can evade person search, or impersonate a target person to fool deep re-ID models in physical world. We evaluate the effectiveness of our transformable patterns on adversaries' clothes with Market1501 and our established PRCS dataset. The experimental results show that the rank-1 accuracy of re-ID models for matching the adversary decreases from 87.9% to 27.1% under Evading Attack. Furthermore, the adversary can impersonate a target person with 47.1% rank-1 accuracy and 67.9% mAP under Impersonation Attack. The results demonstrate that deep re-ID systems are vulnerable to our physical attacks.
Robust Modulation Classification Over $\alpha$-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis

This paper introduces a novel automatic modulation classification (AMC) method using the graph-based fractional lower-order cyclic-spectrum analysis in the α-stable noise environment. The noise in the practical communication scenario usually exhibits impulse characteristics in the statistical sense, which could be modeled as the α-stable distribution. This would make the secondor higher-order statistics of the received signal vanish, and thus the performances of the conventional AMC algorithms designed for Gaussian noise significantly deteriorate when directly employed in the α-stable noise. In our proposed framework, the fractional lower-order cyclic spectrum (FLOCS) analysis is first invoked to acquire the polyspectra of the signal corrupted by the α-stable noise. Then, the graph-based AMC mechanism is systematically established upon the graph representation of the FLOCS to identify the modulation type according to the discrepancies between the graph features derived from the training and test data. The performance of our proposed new algorithm is theoretically analyzed, and the correct classification probability Pcc over the modulation candidate set is formulated analytically. The remarkable scalability and efficiency of our proposed approach for the modulation candidate set variation are also theoretically investigated in detail. Monte Carlo simulation results demonstrate the effectiveness and superiority of the proposed AMC scheme.
Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism

Currently, various attention-based neural networks have achieved successes in sentiment classification tasks, as attention mechanism is capable of focusing on those words contributing more to the sentiment polarity prediction than others.However, the major drawback of these approaches is that they only pay attention to the words, the sentimental information contained in the part-of-speech(POS) is ignored.To address this problem, in this paper, we propose Part-of-Speech based Transformer Attention Network(pos-TAN).This model not only uses the Self-Attention mechanism to learn the feature expression of the text but also incorporates the POS-Attention, which uses to capture sentimental information contained in part-of-speech.In addition, our innovative introduction of the Focal Loss effectively alleviates the impact of sample imbalance on model performance.We conduct substantial experiments on various datasets, and the encouraging results indicate the efficacy of our proposed approach.
Cyber security challenges and vulnerability assessment in the construction industry

The construction industry is making a shift towards digitization and automation (known as Construction 4.0) due to the rapid growth of information and communication technologies as well as 3D printing, mechatronics, machine learning, big data, and the Internet of Things (IoT).These technologies will transform the design, planning, construction, operation and maintenance of the civil infrastructure systems, with a positive impact on the overall project time, cost, quality, and productivity.These new technologies will also make the industry more connected, and the consideration of cybersecurity of paramount importance.Although many studies have proposed frameworks and methodologies to develop such technologies, investigation of cybersecurity implications and related challenges have received very less attention.Some work has focused on security-minded BIM, but it lacks generality or does not consider an approach to determine the vulnerability of the different project participants, construction processes, and products involved during the different phases of construction projects.To address these limitations, this study a) develops a framework to identify cybersecurity risks in the construction industry, and b) assesses the vulnerability of traditional and hybrid delivery methods based on an agent based model (ABM).That is, the vulnerability of different project participants and construction entities during the different phases of the life-cycle of construction projects as a consequence of Construction 4.0.The findings from this study help to identify potential risks and provide a basis to assess the impact of interactions in a digital environment among different project participants.Future work aims to thoroughly investigate the proposed ABM approach and extend the same to other project delivery methods and information exchange networks in construction projects.
A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes

Frauds and default payments are two major anomalies in credit card transactions. Researchers have been vigorously finding solutions to tackle them and one of the solutions is to use data mining approaches. However, the collected credit card data can be quite a challenge for researchers. This is because of the data characteristics that contain: (i) unbalanced class distribution, and (ii) overlapping of class samples. Both characteristics generally cause low detection rates for the anomalies that are minorities in the data. On top of that, the weakness of general learning algorithms contributes to the difficulties of classifying the anomalies as the algorithms generally bias towards the majority class samples. In this study, we used a Multiple Classifiers System (MCS) on these two data sets: (i) credit card frauds (CCF), and (ii) credit card default payments (CCDP). The MCS employs a sequential decision combination strategy to produce accurate anomaly detection. Our empirical studies show that the MCS outperforms the existing research, particularly in detecting the anomalies that are minorities in these two credit card data sets.
Multi-Factorial Comparison for 24 Distinct Transposition Models for Inclined Surface Solar Irradiance Computation in the State of Palestine: A Case Study

Solar energy industries region (MENA). Therefore, this article serves two main objectives. Firstly, it compares comprehensively 24 different transposition models. Several statistical methods as Root Mean Square Error (RMSE), Mean Bias Error (MBE) and Percentage Average Deviation (PAD) and t-statistics (t-stat) are used to quantify the performance of the transposition tilted surface models. Furthermore, the transposition models are compared with real measured hourly timeseries data during 15 months (June 2017 to August 2018) for several Palestinian cities (Gaza and Rafah -in Gaza Strip and Herbon, Jericho, Tulkarm and Nablus -in West Bank) to identify the promising and most accurate model. The analysis was carried out on three bases: annually, monthly and clearness index. The transposition models prove their ability to represent the measured data during the annual and monthly based analysis, but they all failed to achieve complacency in the clearness index (Kt) for the clear sky condition (Kt>0.78).HDKR model is considered the most accurate model for depicting the sky diffuse inclined solar irradiance for majority of Palestinian territories. However, for Nablus the Skartveit-Olseth model has require accurate estimation for global solar irradiation particularly on inclined planes. This improves the accuracy of the sizing procedures and optimizes the performance of the solar energy platforms: as photovoltaic modules and flat-plate solar collectors. Variety of the transposition models are developed and reported to determine solar irradiance incident on an inclined surface. However, there is lack in the literature regarding identifying the most promising transposition model particularly for Middle East and North Africa better overall performance than the other candidate models. It even produces 3.4% better than the HDKR model in predicating solar irradiance for inclined plane. Secondly, the article advises simple, reliable and accurate transposition models for the area of MENA for clear sky conditions. The proposed model was tested for the sites under investigation. The proposed model produces significant better performance than the candidate transposition models, as RMSE and PAD values revealed.
A Gravitational Search Algorithm With Chaotic Neural Oscillators

Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.
A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network

The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is used as the input of the InceptionResNetV2 network to identify different kinds of signals. Transfer adaptation is used for feature extraction and SVM classifier is used to identify the modulation mode of digital signal. The constellation diagram of three typical signals, including Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK) and 8 Phase Shift Keying(8PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4dB, the recognition rates of BPSK, QPSK and 8PSK are respectively 1.0, 0.9966 and 0.9633 obtained by InceptionResnetV2-TA, and at the same time, the recognition rate can be 3% higher than other algorithms. Compared with the traditional modulation recognition algorithms, the experimental results show that the proposed algorithm in this paper has a higher accuracy rate for digital signal modulation recognition at low SNR.
Quantum Temporal Superposition: The Case of Quantum Field Theory

Quantum field theory is completely characterized by the field correlations between spacetime points. In turn, some of these can be accessed by locally coupling to the field simple quantum systems, a.k.a. particle detectors. In this work, we consider what happens when a quantum-controlled superposition of detectors at different space-time points is used to probe the correlations of the field. We show that, due to quantum interference effects, two detectors can gain information on field correlations which would not be otherwise accessible. This has relevant consequences for information theoretic quantities, like entanglement and mutual information harvested from the field. In particular, the quantum control allows for extraction of entanglement in scenarios where this is otherwise provably impossible.
Student perceptions on using cell phones as learning tools

Purpose This paper aims to examine students’ perception, views and opinions about the usage of mobile phones in an educational setting in a higher education institution located in Jamaica. The results of these findings were used to gain insights and to assist education policymakers in adopting most suitable approaches to integrating mobile technology in learning. Design/methodology/approach A survey approach was used for this study. A total of 145 students participated in the study using structured questionnaire design containing 14 questions. Findings The results indicated an overall positive student perception toward cell phones usage as a learning tool and integrating cell phones into learning activities. Students were keen on its usage as a social connectivity and collaborative tool, which they can use for flexible and personalized learning activities. Originality/value Less research has been done in Caribbean and developing countries in analyzing student perception toward using cell phones for learning purposes. This research provides insights in developing policies to assist with the integration of mobile phone technology in learning and how institutions can respond to the advent of advancing mobile technology.
The Effects of VR Environments on the Acceptance, Experience, and Expectations of Cultural Heritage Learning

This article attempts to understand how present Virtual Reality (VR) environments can contribute to enhancing the communication of cultural heritage by providing an experience of the past that is acceptable for the younger generation and how museums and cultural institutions should adopt and use such technologies. Aspects of acceptance, experience, and expectation of VR with the underlying values are not well understood but are important for the sustainability of the communication of cultural heritage as a bequest to future generations. We conducted a combined quantitative--qualitative study on the participants who have various prior experience with gaming and VR, and different levels of knowledge on the history presented within the virtual environment. This study investigates how participants accept and are stimulated in terms of personal experience and their expectations and ideas for the future of museums if VR is used for enhancing the learning of cultural heritage. Prior gaming and VR experience were investigated to see whether they do indeed influence the preference for using VR for learning cultural heritage. We demonstrated that particular age groups and background are especially agreeable to virtual reality as environments for learning and experiencing cultural heritage, regardless of their knowledge of the historical context of the virtually reconstructed site. Our findings also revealed important behaviours in our demographics group with regards to user preferred length of time and the believability of the virtual environment and how it influences aspects of their experience such as the exploration of the heritage site, familiarity, and meaning making. The study has implications for the use of VR for enhancing the experience of cultural heritage in museums and cultural institutions.
Value of Public Challenges for the Development of Pathology Deep Learning Algorithms

The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology. To date, 24 public challenges related to pathology tasks have been published. This article discusses these public challenges and briefly reviews the underlying characteristics of public challenges and why they are helpful to the development of digital tools.