Reviewerly/exHarmony huggingface dataset:
An English-Chinese Machine Translation and Evaluation Method for Geographical Names

In recent years, with increasing international communication and cooperation, the consensus of toponymic information among different countries has become increasingly important. A large number of English geographical names are in urgent need of translation into Chinese, but there are few studies on machine translation of geographical names at present. Therefore, this paper proposes a method of automatically translating English geographical names into Chinese. First, the lexical structure of the geographic names is analyzed to divide the whole name into two parts, the special name and the general name, in an approach based on the statistical template model that implements pointwise mutual information and a directed acyclic graph data structure on the extracted names from different categories of a geographical name corpus. Second, the two parts of the geographic names are translated. The general name can be directly translated via methods of free translation. For the transliteration of the special name, the phonetic symbols are generated based on the cyclic neural network, and then, the syllables are divided based on the minimum entropy and converted into Chinese characters. Finally, the two parts of Chinese characters are combined, and criteria are prepared to evaluate the translation reliability according to the translation process to realize automatic quality inspection and screening of geographical names. As the experimental results show, the method is effective in the translation process of English geographic names into Chinese. This method can be easily extended to other languages such as Arabic.
A meritocratic trust-based group formation in an IoT environment for smart cities

Smart cities are built on top of heterogeneous IoT infrastructures, that can be viewed as communities of software agents (the intelligent objects) that interact with each other to realize complex activities. These agents operate on behalf of users that need services; for these reason agents are often in competition with each other. On the other hand, an agent can often benefit from collaborating with other agents in some circumstances, exchanging information and services. Under this viewpoint, the task of finding the best partners to collaborate is a key task for an agent. A general consensus exists about the benefits deriving by forming friendships and groups for mutual cooperation inside competitive Multi-Agent Systems (MASs). In this respect, the existing proposals are usually addressed to maximize the profit at the level of individual agent or group. Unfortunately, the most part of these approaches could advantage the most aggressive agents, also in presence of bad social behaviors. This is not a desired scenario in a smart city environment. A possible solution to this problem is that of promoting correct behaviors and meritocracy inside agent communities. To this aim, we propose to model the competitive MAS scenario in the framework of non cooperative games by assuming to represent (i) the trustworthiness of agents relationships by means of a trust model and (ii) the capability of a community to provide its members with a good environment by means of its social capital. As a result, a group formation algorithm capable to asymptotically maximize the social capital is proposed. This algorithm highlights two main features: (i) the computed solution is a Nash equilibrium in the considered game and (ii) the only rewarded agents are those having the most correct behaviors.
Antisemitism on Twitter: Collective Efficacy and the Role of Community Organisations in Challenging Online Hate Speech

In this article, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyze the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive for a longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors—that is, Jewish organizations—had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organizations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organizations in reducing the propagation of cyberhate and increasing trust in SM platforms.
Noise-Robust Pupil Center Detection Through CNN-Based Segmentation With Shape-Prior Loss

Detecting the pupil center plays a key role in human-computer interaction, especially for gaze tracking.The conventional deep learning-based method for this problem is to train a convolutional neural network (CNN), which takes the eye image as the input and gives the pupil center as a regression result.In this paper, we propose an indirect use of the CNN for the task, which first segments the pupil region by a CNN as a classification problem, and then finds the center of the segmented region.This is based on the observation that CNN works more robustly for the pupil segmentation than for the pupil center-point regression when the inputs are noisy IR images.Specifically, we use the UNet model for the segmentation of pupil regions in IR images and then find the pupil center as the center of mass of the segment.In designing the loss function for the segmentation, we propose a new loss term that encodes the convex shape-prior for enhancing the robustness to noise.Precisely, we penalize not only the deviation of each predicted pixel from the ground truth label but also the non-convex shape of pupils caused by the noise and reflection.For the training, we make a new dataset of 111,581 images with hand-labeled pupil regions from 29 IR eye video sequences.We also label commonly used datasets (ExCuSe and ElSe dataset) that are considered real-world noisy ones to validate our method.Experiments show that the proposed method performs better than the conventional methods that directly find the pupil center as a regression result.
Distributed representation and one-hot representation fusion with gated network for clinical semantic textual similarity

Abstract Background Semantic textual similarity (STS) is a fundamental natural language processing (NLP) task which can be widely used in many NLP applications such as Question Answer (QA), Information Retrieval (IR), etc. It is a typical regression problem, and almost all STS systems either use distributed representation or one-hot representation to model sentence pairs. Methods In this paper, we proposed a novel framework based on a gated network to fuse distributed representation and one-hot representation of sentence pairs. Some current state-of-the-art distributed representation methods, including Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory networks (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT), were used in our framework, and a system based on this framework was developed for a shared task regarding clinical STS organized by BioCreative/OHNLP in 2018. Results Compared with the systems only using distributed representation or one-hot representation, our method achieved much higher Pearson correlation. Among all distributed representations, BERT performed best. The highest Person correlation of our system was 0.8541, higher than the best official one of the BioCreative/OHNLP clinical STS shared task in 2018 (0.8328) by 0.0213. Conclusions Distributed representation and one-hot representation are complementary to each other and can be fused by gated network.
Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.
A Survey of Marker-Less Tracking and Registration Techniques for Health &amp; Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.
Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

The Location-Based Service has been widely used for mobile communication networks and location systems. However, privacy disclosure for incomplete collection location data in LBS was ignored in most of the existing works. To solve the problem of privacy disclosure, we propose a location privacy method based k-anonymity to prevent privacy disclosure in LBS constrained in incomplete data collection. The proposed scheme can provide effectively location privacy-preserving in the process of constructing the anonymous set, and against background attacks. In this method, we first designed a construct method for anonymous candidate set(ACS) with compressing sensing technology, to solve the problem of incomplete data of collection location. To prevent the privacy disclosure in the process of construct anonymous, we then adapt the differential privacy mechanism to construct the anonymous set(AS) with the ACS. we finally used the optimization method based on the Stackelberg game model to improve the privacy level of AS to against probabilistic attack. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in terms of privacy degree and applicability.
Improving Image Captioning Evaluation by Considering Inter References Variance

Evaluating image captions is very challenging partially due to the fact that there are multiple correct captions for every single image. Most of the existing one-to-one metrics operate by penalizing mismatches between reference and generative caption without considering the intrinsic variance between ground truth captions. It usually leads to over-penalization and thus a bad correlation to human judgment. Recently, the latest one-to-one metric BERTScore can achieve high human correlation in system-level tasks while some issues can be fixed for better performance. In this paper, we propose a novel metric based on BERTScore that could handle such a challenge and extend BERTScore with a few new features appropriately for image captioning evaluation. The experimental results show that our metric achieves state-of-the-art human judgment correlation.
Modeling Label Semantics for Predicting Emotional Reactions

Predicting how events induce emotions in the characters of a story is typically seen as a standard multi-label classification task, which usually treats labels as anonymous classes to predict.They ignore information that may be conveyed by the emotion labels themselves.We propose that the semantics of emotion labels can guide a model's attention when representing the input story.Further, we observe that the emotions evoked by an event are often related: an event that evokes joy is unlikely to also evoke sadness.In this work, we explicitly model label classes via label embeddings, and add mechanisms that track label-label correlations both during training and inference.We also introduce a new semi-supervision strategy that regularizes for the correlations on unlabeled data.Our empirical evaluations show that modeling label semantics yields consistent benefits, and we advance the state-of-theart on an emotion inference task.
Towards Global Explanations of Convolutional Neural Networks With Concept Attribution

With the growing prevalence of convolutional neural networks (CNNs), there is an urgent demand to explain their behaviors. Global explanations contribute to understanding model predictions on a whole category of samples, and thus have attracted increasing interest recently. However, existing methods overwhelmingly conduct separate input attribution or rely on local approximations of models, making them fail to offer faithful global explanations of CNNs. To overcome such drawbacks, we propose a novel two-stage framework, Attacking for Interpretability (AfI), which explains model decisions in terms of the importance of user-defined concepts. AfI first conducts a feature occlusion analysis, which resembles a process of attacking models to derive the category-wide importance of different features. We then map the feature importance to concept importance through ad-hoc semantic tasks. Experimental results confirm the effectiveness of AfI and its superiority in providing more accurate estimations of concept importance than existing proposals.
How do the digital competences of students in vocational schools differ from those of students in cooperative higher education institutions in Germany?

Abstract Digital skills are thought to be a key competence of the twenty-first century. With the rapid growth of internet and communication tool (ICT) usage among both students and teachers, cooperative (co-op) education programmes, like other educational institutions, face the challenge of integrating and supporting digital skill development. However, little is known about how these skills are developed prior to entering cooperative education programmes. Against this background, a sample of 893 freshers, ranging from vocational students to co-op students, were tested according to their digital skills. The analysis shows that co-op students have a higher level of competence than vocational students. In addition, we found that in our sample social background has no impact on digital competences. The results are discussed and classified in the context of the current state of research.
Robot Object Retrieval with Contextual Natural Language Queries

Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments.Previous work has primarily focused on commands specifying the desired object's type such as "scissors" and/or visual attributes such as "red," thus limiting the robot to only known object classes.We develop a model to retrieve objects based on descriptions of their usage.The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects and selects the object that best satisfies the task specified by the verb.Our model directly predicts an object's appearance from the object's use specified by a verb phrase.We do not need to explicitly specify an object's class label.Our approach allows us to predict high level concepts like an object's utility based on the language query.Based on contextual information present in the language commands, our model can generalize to unseen object classes and unknown nouns in the commands.Our model correctly selects objects out of sets of five candidates to fulfill natural language commands, and achieves an average accuracy of 62.3% on a held-out test set of unseen ImageNet object classes and 53.0% on unseen object classes and unknown nouns.Our model also achieves an average accuracy of 54.7% on unseen YCB object classes, which have a different image distribution from ImageNet objects.We demonstrate our model on a KUKA LBR iiwa robot arm, enabling the robot to retrieve objects based on natural language descriptions of their usage 1 .We also present a new dataset of 655 verb-object pairs denoting object usage over 50 verbs and 216 object classes 2 .
Humboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods

Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha−1. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allowed reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders.
Security Properties of Light Clients on the Ethereum Blockchain

Ethereum is a decentralized blockchain, known as being the second most popular public blockchain after Bitcoin. Since Ethereum is decentralised the canonical state is determined by the Ethereum network participants via a consensus mechanism without a centralized coordinator. The network participants are required to evaluate every transaction starting from the genesis block, which requires a large amount of network, computing, and storage resources. This is impractical for many devices with either limited computing resources or intermittent network connectivity. To overcome this drawback Ethereum defines a light client protocol where the light client fetches the blockchain state from a node operating as a light protocol server. Light clients are unable to maintain blockchain state internally, and as a consequence can only perform partial validation on blocks. Thus they rely on the light server for full block validation and to provide the updated blockchain state. Light clients connect to multiple light servers to mitigate the risk of relying on a single potentially dishonest server. Ethereum light clients are known to suffer from a probabilistic security model, but they are widely assumed to be secure under normal operating conditions. In fact, the implicit security assumptions of light clients have not been formally characterised in the literature. We present and analyse the probabilistic security guarantees under three different adversarial scenarios. The results show that for any adversary that is able to manipulate the network, the security assurances provided by the light protocol are severely impacted, and in some cases entirely lost. These results clearly demonstrate that the assumption of normal operating conditions is insufficient to justify the security assumptions of light clients. Our work also provides insight to the security of light clients under different security parameters, allowing light client implementers to more accurately understand the potential security trade-offs.