Reviewerly/exHarmony huggingface dataset:
What Object-Oriented Programming Was Supposed to Be: Two Grumpy Old Guys’ Take on Object-Oriented Programming

Object-oriented programming has been around for more than 50 years and is now the most dominant style of programming. In recent years there has been an increasing criticism of object-oriented programming. Some people argue that the mainstream object-oriented languages do not capture the intentions of object-orientation as intended by its founding fathers. There are indeed issues with object-orientation as practiced by mainstream. In this essay, we identify a number of issues that we think are problematic. We argue that the primary reason for these issues is that reuse is considered the main advantage of object-orientation at the expense of modeling. We argue that modeling should be the main focus, that programming is modeling, and we describe a number of principles to follow when practicing object-oriented modeling.
FPGA Implementation of a Digital Watermarking System using dwt Transformation

In this work, a hardware implementation of an image watermarking technique is presented, the scheme is based on the Discrete Wavelet Transform (DWT). The embedding process is made in the transform domain, using one-level DWT coefficients we insert the watermark image into the host image and we return to the spatial domain to transmit the data. In the detection stage we adopt a non-blind technique to extract the watermark from the DWT coefficients after one-level transform using the host image. A hardware co-design technique is developed, using FPGA, to accelerate image authentication. Comparing with other achievements, our proposed schemes shows some improvement in term of resources consumption. Several metrics were used to demonstrate the suggested architecture’s effectiveness in terms of visibility and robustness against various threats.
Breast lesion detection and visualization utilizing artificial intelligence and the H-scan

We incorporated raw ultrasound parameters into artificial intelligence-based breast cancer diagnosis to achieve improved accuracy compared to radiologists and deep learning (DL). 76 patients with suspicious breast lesions were ultrasound-imaged using a Samsung RS85 system equipped with a 9.4 MHz center frequency transducer. The patients underwent biopsy, and the biopsy results were used as a reference gold standard: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{n}{=53}$</tex> for benign and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{n}{=23}$</tex> for malignant. Ten radiologists reviewed the ultrasound images and provided BI-RADS (Breast Imaging Reporting and Data System) scores. A previously trained DL product with a modified fully convolutional network and GoogLeNet contoured the breast lesion boundaries. Within the contoured lesions, ultrasound parameters were extracted from the radiofrequency, envelope, and log-compressed data: (1) H-scan color level, (2) lesion boundary shape using convex hull, (3) B-mode boundary standard deviation (STD), (4) B-mode STD, and (5) Burr distribution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{b}$</tex> . To quantify breast condition, multiparametric analysis combining the 5 features was performed using principal component analysis, resulting in the first principal component (PC1). The PC1 outputs within a lesion were overlaid on B-mode images. We calculated the area under the curve (AVC) to evaluate performance. We compared AVC results from radiologists, the DL product, and our PC1 quantification. The PC1 showed the highest AUC. Further, utilizing this PC1 quantification, we visualized the localized probability of malignancy, illustrating BI-RADS score differences using a color display. Overall, we demonstrated the potential of utilizing raw ultrasound parameters to improve DL performance and to achieve higher diagnostic accuracy than radiologists for breast cancer.
How broadcasters enhance rapport with viewers in live streaming commerce

Abstract Despite the increasing research interest, the existing literature on live streaming commerce as a new e-commerce mode is still in its infancy. Based on 100 live streaming commerce videos from the top two broadcasters on Taobao Live in China, this study conducted a genre-based discourse analysis to investigate the move pattern in this new genre. The study draws on rapport management theory to scrutinize the linguistic functions of the moves, to explore how the broadcasters managed to enhance broadcaster-viewer relationship. Our findings may further our understanding of live streaming commerce as a new form of digital genre, and shed light on how successful broadcasters may strategically manage their relationship with viewers through well-organized discourse forms. Theoretically, the present research may contribute to the literature of employing rapport management theory in the discourse domain by extending it into a new digital genre analysis. Practically, our findings may provide implications for relevant practitioners.
Resourcefulness, narratives, and identity in science, technology, engineering, arts and mathematics education: A perspective of makerspaces for rural communities in Colombia

Disparities of scientific education (STEM) in rural and remote communities are exacerbating inequalities and taking away opportunities for youth to explore their scientific interests and participate in science-making globally. Maker education in rural and remote areas of Latin America could boost the participation of rural communities in scientific education and open alternatives to the lack of higher literacy rates and STEM education. In Colombia, tertiary education has been making efforts to close this gap through the Itinerant Technical Academies of Servicio Nacional de Aprendizaje (SENA: National Learning Service). Participants in the Technical Academies create relevant technologies for their context, reflecting on their cultural values and aesthetics. In this contribution, we describe three projects conducted in the itinerant Technical Academies of rural Colombia in Narino to exemplify how participants made their artifacts relevant to their culture using available resources, materials, and practices. Students are using available resources to create artifacts that are relevant to their social contexts and aligned with constructivist practices. We propose and discuss three characteristics that have emerged in makerspaces situated in rural Colombia: (i) Narratives, (ii) Resourcefulness, and (iii) Identities and we suggest that makers, scholars, policymakers, and educators should pay attention to those three elements to guide future studies and further research on maker education in the Global South.
Information Sharing Game and Value Analysis for the Following Enterprise Applications of Blockchain Technology

To address the question of whether small and medium-sized enterprises (SMEs) should follow benchmark companies in establishing a blockchain-based information sharing platform, a two-tier supply chain decision-making problem dominated by retailers and containing two manufacturers of unequal status is analyzed, including the impact on the utility and social welfare of different game players in the supply chain and the supply chain as a whole, taking into account the fact that consumers are sensitive to both price and quality. For this purpose, traditional cooperation models, short-term and long-term models based on blockchain technology for information sharing were constructed and solved, respectively. The findings suggest that in the short term, the establishment of a blockchain technology-based information sharing platform will widen the status gap between manufacturers, making the strong stronger and the weak weaker, with retailers’ profits and social welfare suffering and no change in consumer surplus. In the long term, the quality improvement effect of information sharing will narrow the gap between manufacturers and increase members’ profits, with retailers and the entire supply chain experiencing a significant increase in profits after a short period of time, as well as an increase in consumer surplus and social welfare. In addition, when consumer price sensitivity and quality sensitivity are high, the establishment of a blockchain technology-based information sharing platform is beneficial for the stronger manufacturers, detrimental to the weaker manufacturers, and beneficial for the supply chain in the long term; otherwise, the absence of a blockchain technology-based information sharing platform is beneficial for the weaker manufacturers and the supply chain.
How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis

Serendipity is one of beyond-accuracy objectives for recommender systems (RSs), which aims to achieve both relevance and unexpectedness of recommendations, so as to potentially address the "filter bubble" issue of traditional accuracy-oriented RSs. However, so far most of the serendipity-oriented studies have focused on developing algorithms to consider various types of item features or user characteristics, but are largely based on their own assumptions. Few have stood from users' perspective to identify the effects of these features on users' perceptions of the serendipity of the recommendation. Therefore, in this paper, we have analyzed their effects with two user survey datasets. These are the Movielens Serendipity Dataset of 467 users' responses to a retrospective survey of their perceptions of the recommended movie's serendipity, and the Taobao Serendipity Dataset of 11,383 users' perceptions of the serendipity of a recommendation received at a mobile e-commerce platform. In both datasets, we have analyzed the correlations between users' serendipity perceptions and various types of item features (i.e., item-driven such as popularity, profile-driven such as in-profile diversity, and interaction-driven including category-level and item-level features), as well as the influence of several user characteristics (including the Big-Five personality traits and curiosity). The results disclose both domain-independent and domain-specific observations, which may be constructive in enhancing current serendipity-oriented recommender systems by better utilizing item features and user data.
Robust Sequential Fusion Estimation Based on Adaptive Innovation Event-Triggered Mechanism for Uncertain Networked Systems

In order to reduce the transmission pressure of the networked system and improve its robust performance, an adaptive innovation event-triggered mechanism is designed for the first time, and based on this mechanism, the robust local filtering algorithm for the multi-sensor networked system with uncertain noise variances and correlated noises is presented. To avoid calculating the complex error cross-covariance matrices, applying the sequential fusion idea, the robust sequential covariance intersection (SCI) and sequential inverse covariance intersection (SICI) fusion estimation algorithms are proposed, and their robustness is analyzed. Finally, it is verified in the simulation example that the proposed adaptive innovation event-triggered mechanism can reduce the communication burden, the robust local filtering algorithm is effective for the uncertainty generated by the unknown noise variances, and two robust sequential fusion estimators show good robustness, respectively.
Robustness Analysis and Enhancement of Deep Reinforcement Learning-Based Schedulers

Dependency-aware jobs, such as the big data analytic workflows, are commonly executed on the cloud. They are compiled to directed acyclic graphs, with tasks linked in regarding the dependency. The cloud scheduler, which maintains a large number of resources, is responsible to execute tasks in parallel. To resolve the complex dependencies, Deep Reinforcement Learning (DRL) based schedulers are widely applied. However, we find that the DRL-based schedulers are vulnerable to the perturbations in the input jobs and may generate falsified decisions to benefit a particular job while delaying the others. By perturbation, we mean a slight adjustment to the job's node features or dependencies, while not changing its functionality. In this paper, we first explore the vulnerability of DRL-based schedulers to job perturbations without accessing the information of the DRL models used in the scheduler. We devise the black-box perturbation system, in which, a proxy model is trained to mimic the DRL-based scheduling policy. We show that the high-faith proxy model can help to craft effective perturbations. The DRL-based schedulers can be as high as 60% likely to be badly affected by the perturbations. Then, we investigate the solution to improve the robustness of DRL-based schedulers to such perturbations. We propose an adversarial training framework to force the neural model to adapt to the perturbation patterns during training so as to eliminate the potential damage during applications. Experiments show that the adversarial-trained scheduler is more robust, reducing the chance of being affected to 3-fold less and the potential bad effects halved.
DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf radio circuitry, RFP techniques can identify wireless devices directly at the physical layer, allowing low-latency low-energy cryptography-free authentication. However, existing Wi-Fi RFP techniques are based on software-defined radio (SDRs), which may ultimately prevent their widespread adoption. Moreover, it is unclear whether existing strategies can work in the presence of MU-MIMO transmitters – a key technology in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require SDR technologies and can be run on any low-cost Wi-Fi device to authenticate MU-MIMO transmitters. Our key intuition is that imperfections in the transmitter’s radio circuitry percolate onto the beamforming feedback matrix, and thus RFP can be performed without explicit channel state information (CSI) computation. DeepCSI is robust to inter-stream and inter-user interference being the beamforming feedback not affected by those phenomena. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi radios emit signals in different positions. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The identification accuracy remains above 82% when the device moves within the environment. To allow replicability and provide a performance benchmark, we pledge to share the 800 GB datasets – collected in static and, for the first time, dynamic conditions – and the code database with the community.
Emulation of epidemics via Bluetooth-based virtual safe virus spread: Experimental setup, software, and data

We describe an experimental setup and a currently running experiment for evaluating how physical interactions over time and between individuals affect the spread of epidemics. Our experiment involves the voluntary use of the Safe Blues Android app by participants at The University of Auckland (UoA) City Campus in New Zealand. The app spreads multiple virtual safe virus strands via Bluetooth depending on the physical proximity of the subjects. The evolution of the virtual epidemics is recorded as they spread through the population. The data is presented as a real-time (and historical) dashboard. A simulation model is applied to calibrate strand parameters. Participants' locations are not recorded, but participants are rewarded based on the duration of participation within a geofenced area, and aggregate participation numbers serve as part of the data. The 2021 experimental data is available as an open-source anonymized dataset, and once the experiment is complete, the remaining data will be made available. This paper outlines the experimental setup, software, subject-recruitment practices, ethical considerations, and dataset description. The paper also highlights current experimental results in view of the lockdown that started in New Zealand at 23:59 on August 17, 2021. The experiment was initially planned in the New Zealand environment, expected to be free of COVID and lockdowns after 2020. However, a COVID Delta strain lockdown shuffled the cards and the experiment is currently extended into 2022.
Unsupervised Feature Selection Using Iterative Shrinking and Expansion Algorithm

In this article, we propose an unsupervised feature selection algorithm based on the recently developed shrinking and expansion algorithm (SEA). The SEA is already an established, efficient algorithm to identify dense subgraphs within a weighted graph. To conduct SEA on a weighted graph, the dataset is first mapped onto an equivalent graph notation where each vertex corresponds to a feature, and the weight of each edge represents the normalized mutual information between the associated two features that form the edge. As a result, the feature selection problem is presented as the best way to obtain several dense sub-feature spaces by using the SEA on a weighted feature graph. The proposed feature selection algorithm consists of a two-stage structure, in which the first stage is utilized to find a number of dense feature subgraphs, whereas the second stage is used to identify a representative feature from each of these feature subgraphs. The main advantage of the proposed algorithm is that the users are not required to provide the number of features to be selected, which is a major flaw in most of the existing algorithms. The current findings from the researchers' method are better than the other state-of-the-art algorithms because the feature selection process identifies approximately one-fifth to one-third of the number of original features while providing a high accuracy score. The superiority of the proposed algorithm over the other conventional methods of feature selection is established for a number of real-life datasets. In addition, the performance of the proposed feature selection algorithm is also found to be better than other algorithms in one of the innovative application areas of computational intelligence such as ambient intelligence.
A survey of document-level relation extraction

Relation extraction is the core mission and a great significant part of natural language processing. This paper briefly expound the development of relation extraction and introduces the commonly used document-level relation extraction datasets and evaluation indexes of model effects. According to the different representation of entity by model, documentlevel relation extraction can be divided into sequential method and graph method. Besides, we conduct contrastive analysis concerning different relation extraction models, and compared the effects of various relation extraction models. Finally, we summarize the key research contents of document-level relation extraction in the future and forecast the development trend.
Comparative analysis of video anomaly detection algorithms

With the continuous development of deep learning, anomaly detection technology in computer vision has maderemarkable progress. Video anomaly detection is essential to ensure public safety. We classify and summarize anomalydetection based on deep learning. First, the overall process of anomaly detection is presented. Then, based on the neural network training method, we discuss the development and application of deep learning in the field of anomaly detectionfrom four aspects: Multiple Instance Learning, Regression models, Clustering models, and Reconstruction models. Finally, we present commonly used datasets and performance evaluation criteria , analyze the performance of different algorithms and discuss the future directions.
Eliminating domain deviation via synthetic data for vehicle re-identification

Vehicle re-identification (Re-ID) methods with supervised learning achieve high accuracy, but rely heavily on effective supervised labels, so that it cannot extend them to the unsupervised domain. Since the vehicle presents great changes from different perspectives and the large distribution gap between different datasets. Therefore, how to design a vehicle Re-ID method on label-free datasets and show outstanding performance is a difficult problem. In this paper, we propose an unsupervised vehicle Re-ID framework based on synthetic data. Our proposed framework consists of three steps: (1)we use synthetic data to generate pseudo-target samples similar in style to the target domain and use them for model pre-train; (2)the pre-train model is fine-tuned by the source and target domain to improve the cross-domain generalization of the model; (3)the orientation and the camera similarity are calculated by the pre-train orientation and the camera model of the synthetic data, thus punishing the final similarity. Experiments show that the proposed method outperforms existing stateof-the-art methods on benchmark datasets.
Prediction of Malaria incidence in mainland China

Globally, the malaria epidemic remains serious, which poses an enormous threat to human health. Although China has made remarkable progress in the prevention and control of malaria, it still faces the potential for imported risks of this infectious disease. In this study, the SVR, GM(1,1), LR, and Exponential Smoothing models were applied respectively to predict the incidence of Malaria in mainland China. The results showed that the SVR model was the optimum model, and its prediction performance has higher than that of GM(1,1), LR, and Exponential Smoothing models.
AlphaStar: an integrated application of reinforcement learning algorithms

Deep Reinforcement Learning (DRL) is poised to revolutionize the field of AI and represents a step towards general intelligence. Currently, AlphaStar achieved the Grandmaster level in StarCraft gaming, which is a remarkable breakthrough in Real-Time Strategy (RTS) gaming. The paper discusses the general framework of RTS gaming agents, and presents the method innovation from baseline RL algorithms to the AlphaStar. We begin with the basic idea and taxonomy of DRL, then progress to the technical framework of AlphaStar from the perspective of state space design, action space design, policy and value net framework and multi-agent training methods, presenting key issue and feasible method in full-length scale of RTS gaming. Finally, we draw a brief conclusion.
Generalized Zero-Shot Learning Based on Manifold Alignment

Generalized zero-shot learning is a method that can classify seen and unseen samples by learning training samples’ visual and semantic modal information. Visual modal information is generally extracted by feature extraction networks pre-trained with a large-scale data set, and semantic modal information is typically represented by class attributes. Different categories have shared semantic information, therefore, through learning the mapping between two modal information, the transferable knowledge can be used to classify testing samples. However, most methods align the two modal information of the per-sample rather than considering the alignment of the distribution of multiple instances in the two modalities. We utilize variational autoencoders mapping two modalities’ information to a shared latent space, then align the samples’ manifold structure of them to promote the accuracy of model classification. We evaluate the proposed method on several benchmark datasets (CUB, SUN, and AWA2), and the significant improvements have proved the method’s effectiveness.