Reviewerly/exHarmony huggingface dataset:
Characteristics of Blockchain and Smart Services, for Smart Governments: A systematic review of the literature

The interest in blockchain technology has grown rapidly, day by day. This is simply because of the security and decentralization that it provides. Nevertheless, most government services around the world run on inefficient systems loaded with heavy bureaucracy. They lead to non-transparent systems and a loss of public confidence in government services. The present systematic review of the literature on this topic aims to highlight the characteristics of blockchain technology that demonstrate its uniqueness, together with the characteristics of the smart government services that are required for efficient service delivery. It was found that the dominant characteristics of blockchain technology that are expected to provide the highest value for customers are decentralization and the capacity to be shared and public, whereas the most desired characteristics for the efficient service delivery of smart government services are speed, trust and participation. The paper went on to examine how the use of blockchain technology in government services is impacting on their delivery to customers by using examples from all around the world and to conduct a SWOT analysis of the use of blockchain in the government sector. Its findings are expected to help governments to develop a blockchain strategy that helps smart government services to adopt blockchain successfully.
Learning enhanced features and inferring twice for fine-grained image classification

Abstract Fine-Grained Visual Categorization (FGVC) aims to distinguish between extremely similar subordinate-level categories within the same basic-level category. Existing research has proven the great importance of the discriminative features in FGVC but ignored the contributions for correct classification from other features, and the extracted features always contain more information about the obvious regions but less about subtle regions. In this paper, firstly, a novel module named forcing module is proposed to force the network to extract more diverse features for FGVC, which generates a suppression mask based on the class activation maps to suppress the most distinguishable regions, so as to force the network to extract other secondary distinguishable features as the final features. The forcing module consists of the original branch and the forcing branch. The original branch focuses on the primary discriminative regions while the forcing branch focuses on secondary discriminative regions. Secondly, in order to solve the problem that information of small-scale distinguishable features is lost seriously after multi-layer down-sampling, according to the class activation maps of the first prediction, the object is cropped and scaled as the second input. To reduce the prediction error, the first and second prediction probabilities are fused as the final prediction result. Experimental results indicate that the proposed method not only outperforms the baseline model by a large margin (3.7%, 5.9%, 3.1% respectively) on CUB-200-2011, Stanford-Cars, and FGVC-Aircraft, but also achieves state-of-the-art performance on FGVC-Aircraft.
Determination of Organic Matter by Using Titrimetric and Loss on Ignition Methods for Northern Iraqi Governorates Soils

The study was carried out to determine soil organic matter content (SOM) using loss on ignition (LOI) and titrant methods. Eighty-four composite soil samples were collected from surface soil of randomly different locations in four governorates of northern parts of Iraq. The percent mean values of SOM recorded were 2.34, 2.399 and 1.821, respectively for Erbil, Sulaymaniyah, Kirkuk, and Duhok soil samples for the LOI method, while lower values were obtained 1.39, 1.595 and 0.98% respectively by using the titrant method for the same soil samples. A low relationship between two SOM procedures was obtained (r2=0.462, 0.585 and 0.241) respectively for the same governorates soil samples. Current results revealed that the titrant method is more accurate and convenient to be used for SOM determination of soils in our area, and further studies must be done by using other methods to find the more suitable procedure for this purpose.
Automated segmentation of multiple sclerosis lesions based on convolutional neural networks

Automatic segmentation using Deep Learning (DL) has recently outperformed previous methods. In this paper, we are interested in automatic MS lesion segmentation of 2D images using DL approaches. The main challenge is to propose a new model that uses referenced CNN models such as U-Net, ResNet, and DenseNet, but with less parameters and a faster execution time. We evaluated the performance of our suggested technique, ‘Concat-U-Net’, to that of three implemented models, using two different datasets: our private MS database and the public ISBI. A comparison research was conducted using the Dice, Sensitivity, Precision, and PR-Curve evaluation metrics. On our private data set, the best Dice produced by the suggested technique was 0.80 as well as 0.77, 0.98, and 0.78 for sensitivity, PR-curve, and Precision, respectively. We enhanced MS segmentation results using the ISBI database and acquired a Dice value of 0.72, which is close to the results reported in the literature. For our data set and ISBI database, we lowered the elapsed execution time from 16.99 s to 9.36 s and from 25.02 s to 4.37 s, respectively. Furthermore, by reducing the number of parameters, we achieved an 81.51% time gain.
Leveraging Governance Performance to Enhance Climate Resilience

Abstract Enhancing the resilience of complex social‐ecological systems (SES) to climate change requires transformative changes. Yet, there are knowledge gaps on how best to achieve transformation. In this study, we present an approach for assessing governance performance in SES and identifying leverage points to ultimately enhance climate resilience. The approach combines three different methods including a capital approach framework, fuzzy cognitive mapping, and a leverage points analysis. Using a coastal case‐study in Algoa Bay, South Africa, the performance of governance processes contributing to different forms of capital is assessed. Subsequently, leverage points ‐ where a small shift may lead to transformative changes in the system as a whole ‐ are identified based on measures of centrality and performance. Results suggest that a range of leverage points can improve governance performance and therefore climate resilience in the case‐study. Leverage points include improving (a) support from the provincial government; (b) priority given to climate change in the integrated development plan; (c) frequency of collaborations; (d) participation in the implementation of climate action plans; (e) allocation of funding to climate change actions; (f) the overall level of preparedness in terms of staff with relevant expertise; (g) public awareness and understanding of climate change. The approach can also be used to analyze and model the relations and interactions between capitals. The study advances methodological and theoretical knowledge on the identification of leverage points for enabling transformations toward climate resilience and broader sustainability goals in SES.
Optimal Decision and Coordination of Organic Food Supply Chain from the Perspective of Blockchain

Driven by the pain points of the organic food supply chain, which has been plagued by counterfeiting and difficulties in pursuing accountability, this paper investigates a secondary organic food supply chain consisting of suppliers and retailers and establishes two supply chain models under the traditional model and in the blockchain traceability context. In order to effectively solve the problem of unrealized Pareto improvement in organic food supply chain after applying blockchain, a new hybrid contract based on benefit-sharing and cost-sharing is designed to coordinate the supply chain and realize Pareto improvement, and this solution is gradually applied to organic food enterprises. Based on the fact that blockchain can improve trust in the supply chain and eliminate counterfeiting of organic food, the relationship between the rate of genuine products and market demand and the cost of blockchain is established, and then the analysis is developed using the Stackelberg game. We compare the traditional model with the model in the blockchain context and analyze the optimal profit of each supply chain entity, comparing the change in optimal profit before and after the blockchain implementation, and clarifying the cost threshold of the blockchain technology input application. We find that: (i) The adoption of blockchain can not only improve the authenticity of products and combat counterfeit and shoddy organic food, but at the same time, the improvement of organic level in the context of blockchain will also attract some consumers to buy organic food, which will increase the main body of the supply chain and the overall profit. (ii) Blockchain-adopted supply chains are consistently more profitable for all parties and overall than traditional supply chains. The main contribution of this study is that in the organic food supply chain under the application of blockchain technology model, by introducing revenue-sharing and cost-sharing contracts, the profit between each member of the organic food supply chain is further improved than the traditional model, and also, all of them are optimized, which further improves the stability of the supply chain and brings the supply chain to a coordinated state. Finally, in this context, the obtained results show the effectiveness and realistic operational efficiency of the proposed approach for companies compared to traditional single revenue-sharing covenants. A combination of revenue-sharing and cost-sharing covenants is the best approach to solve such problems. In conclusion, it should be noted that the analysis presented in this study will help decision makers choose the most appropriate option among the possible solutions according to their criteria. This proposed framework can also be extended in various cases where profits are out of balance in the organic food supply chain, such as safety and value gain.
Digital Communication and Multimodal Features: Functioning of Emoji in Interpersonal Communication

Technical advances and digital means of communication have led to the development of digital semiotics which is characterised by its multimodality and abounds in paralinguistic elements such as emojis, emoticons, memes, etc. These extralinguistic elements serve as a compensatory mechanism in the new communication means. The increasing interest of users in various iconic signs and symbols generates the research interest in different fields of knowledge. The study aims to consider cognitive, semiotic and psycholinguistic features of emojis in interpersonal communication through analysing their functions in text messages and in social network messages. An attempt to reveal their persuasive mechanism is made. The research is based on a large scale dataset comprised of the private text messages as well as public posts on social networks which include verbal and nonverbal / iconic elements. The research data presents a multilingual bank of English, Russian and French sources. The research methods include context analysis, linguistic and pragmatic analysis and content analysis. The findings show that emojis in private interpersonal communication perform a number of functions, namely nonverbal, emotive, pragmatic, punctuation, substitutional, decorative and rhetorical functions. These iconic symbols incorporated in the interpersonal digital communication present a compensatory mechanism and the means of persuasion of a message addressee / recipient. The combination of verbal and iconic elements triggers a double focusing mechanism, and the perception is shaped by all cognitive mechanisms including rational and emotional, unconscious components.
Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions

Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge's center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%.
Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures

There is a critical need for detection of administrative regions through network map pictures in map censorship tasks, which can be implemented by target detection technology. However, on map images there tend to be numerous administrative regions overlaying map annotations and symbols, thus making it difficult to accurately detect each region. Using a RetinaNet-based target detection model integrating ResNet50 and a feature pyramid network (FPN), this study built a multi-target model and a single-target cascading model from three single-target models by taking Taiwan, Tibet, and the Chinese mainland as target examples. Two models were evaluated both in classification and localization accuracy to investigate their administrative region detection performance. The results show that the single-target cascading model was able to detect more administrative regions, with a higher f1_score of 0.86 and mAP of 0.85 compared to the multi-target model (0.56 and 0.52, respectively). Furthermore, location box size distribution from the single-target cascading model looks more similar to that of manually annotated box sizes, which signifies that the proposed cascading model is superior to the multi-target model. This study is promising in providing support for computer map reading and intelligent map censorship.
Demonstrating quantum properties of triple photons generated by $$\chi ^3$$ processes

Abstract Triple-photon generation (TPG) is based on a third-order nonlinear optical interaction, which is the most direct way to produce pure quantum three-photon states. These states can exhibit three-body quantum correlations, and their statistics cannot be reproduced by any Gaussian statistics of coherent sources or optical parametric twin-photon generator, making them potentially useful for quantum information processing tasks such as quantum state distillation, quantum error-correction and universal quantum computing. Furthermore, the generation of entangled photon pairs heralded by the detection of a third photon can be used in advanced quantum communication protocols. We made the first experimental demonstration of TPG in 2004 using a bi-stimulation scheme in a bulk KTP crystal, followed by the quantum theory. The new challenges are now to achieve a spontaneous TPG and the corresponding quantum experiments and protocols using oriented ridge KTP waveguides, which ensures both birefringence phase-matching and light confinement. The waveguides are cut by a precision dicing saw. We recently performed their characterization using third-harmonic generation measurements, which showed their good quality. A rate of about 5 triplets per second is expected when pumping a 5-cm-long waveguide with a 5-W 532 nm beam in the CW regime. Such a spontaneous TPG exhibits low rate of triple photons, which makes the certification of quantum features hard. In this article, we review our theoretical and experimental work on TPG and the associated quantum modeling. We also develop theoretical tools for the certification of quantum features of spontaneous triple-photon states. Graphic abstract
MMSports'22

The fifth ACM International Workshop on Multimedia Content Analysis in Sports (ACM MMSports'22) is part of the ACM International Conference on Multimedia 2022 (ACM Multimedia 2022). After two years of pure virtual MMSports workshops due to COVID-19, MMSports'22 is held on-site again. The goal of this workshop is to bring together researchers and practitioners from academia and industry to address challenges and report progress in mining, analyzing, understanding, and visualizing multimedia/multimodal data in sports, sports broadcasts, sports games and sports medicine. The combination of sports and modern technology offers a novel and intriguing field of research with promising approaches for visual broadcast augmentation and understanding, for statistical analysis and evaluation, and for sensor fusion during workouts as well as competitions. There is a lack of research communities focusing on the fusion of multiple modalities. We are helping to close this research gap with this workshop series on multimedia content analysis in sports. Related Workshop Proceedings are available in the ACM DL at: https://dl.acm.org/doi/proceedings/10.1145/3552437.
BetterSight: Immersive Vision Training for Basketball Players

Vision training is important for athletes and is key to win a sports game. Traditional vision training methods are suitable for sports that focus only on the ball. For basketball, however, players need to observe multiple moving objects (i.e., ball and players) concurrently on a large court. We propose BetterSight, an immersive vision training system for basketball that not only trains the vision of the player but also requires the player to dribble the ball stably, mimicking the situation in a real basketball game. BetterSight is composed of an Interaction Module (IM), a Training Content Generation Module (TCGM), and an Analysis Module (AM). IM allows the trainee to interact with the system more intuitively based on gesture and speech rather than the controller. TCGM simulates the training scenarios based on the training configurations selected by the trainee. AM collects the video sequences capturing the trainee and the trainee's eye movements during the training phase, and then analyzes the trainee's gaze, dribbling movements, and number of dribbles. The analyzed data can be used to evaluate the training effectiveness of using the proposed BetterSight.
Balanced Gradient Penalty Improves Deep Long-Tailed Learning

In recent years, deep learning has achieved a great success in various image recognition tasks. However, the long-tailed setting over a semantic class plays a leading role in real-world applications. Common methods focus on optimization on balanced distribution or naive models. Few works explore long-tailed learning from a deep learning-based generalization perspective. The loss landscape on long-tailed learning is first investigated in this work. Empirical results show that sharpness-aware optimizers work not well on long-tailed learning. Because they do not take class priors into consideration, and they fail to improve performance of few-shot classes. To better guide the network and explicitly alleviate sharpness without extra computational burden, we develop a universal Balanced Gradient Penalty (BGP) method. Surprisingly, our BGP method does not need the detailed class priors and preserves privacy. Our new algorithm BGP, as a regularization loss, can achieve the state-of-the-art results on various image datasets (i.e., CIFAR-LT, ImageNet-LT and iNaturalist-2018) in the settings of different imbalance ratios.
Adjustable Memory-efficient Image Super-resolution via Individual Kernel Sparsity

Though single image super-resolution (SR) has witnessed incredible progress, the increasing model complexity impairs its applications in memory-limited devices. To solve this problem, prior arts have aimed to reduce the number of model parameters and sparsity has been exploited, which usually enforces the group sparsity constraint on the filter level and thus is not arbitrarily adjustable for satisfying the customized memory requirements. In this paper, we propose an individual kernel sparsity (IKS) method for memory-efficient and sparsity-adjustable image SR to aid deep network deployment in memory-limited devices. IKS performs model sparsity in the weight level that implicitly allocates the user-defined target sparsity to each individual kernel. To induce the kernel sparsity, a soft thresholding operation is used as a gating constraint for filtering the trivial weights. To achieve adjustable sparsity, a dynamic threshold learning algorithm is proposed, in which the threshold is updated by associated training with the network weight and is adaptively decayed with the guidance of the desired sparsity. This work essentially provides a dynamic parameter reassignment scheme with a given resource budget for an off-the-shelf SR model. Extensive experimental results demonstrate that IKS imparts considerable sparsity with negligible effect on SR quality. The code is available at: https://github.com/RaccoonDML/IKS.
FedZaCt: Federated Learning with Z Average and Cross-Teaching on Image Segmentation

In Federated Learning (FL), data communication among clients is denied. However, it is difficult to learn from the decentralized client data, which is under-sampled, especially for segmentation tasks that need to extract enough contextual semantic information. Existing FL studies always average client models to one global model in segmentation tasks while neglecting the diverse knowledge extracted by the models. To maintain and utilize the diverse knowledge, we propose a novel training paradigm called Federated Learning with Z-average and Cross-teaching (FedZaCt) to deal with segmentation tasks. From the model parameters’ aspect, the Z-average method constructs individual client models, which maintain diverse knowledge from multiple client data. From the model distillation aspect, the Cross-teaching method transfers the other client models’ knowledge to supervise the local client model. In particular, FedZaCt does not have the global model during the training process. After training, all client models are aggregated into the global model by averaging all client model parameters. The proposed methods are applied to two medical image segmentation datasets including our private aortic dataset and a public HAM10000 dataset. Experimental results demonstrate that our methods can achieve higher Intersection over Union values and Dice scores.