Reviewerly/exHarmony huggingface dataset:
Leveraging external resources for offensive content detection in social media

Hate speech is a burning issue of today’s society that cuts across numerous strategic areas, including human rights protection, refugee protection, and the fight against racism and discrimination. The gravity of the subject is further demonstrated by António Guterres, the United Nations Secretary-General, calling it “a menace to democratic values, social stability, and peace”. One central platform for the spread of hate speech is the Internet and social media in particular. Thus, automatic detection of hateful and offensive content on these platforms is a crucial challenge that would strongly contribute to an equal and sustainable society when overcome. One significant difficulty in meeting this challenge is collecting sufficient labeled data. In our work, we examine how various resources can be leveraged to circumvent this difficulty. We carry out extensive experiments to exploit various data sources using different machine learning models, including state-of-the-art transformers. We have found that using our proposed methods, one can attain state-of-the-art performance detecting hate speech on Twitter (outperforming the winner of both the HASOC 2019 and HASOC 2020 competitions). It is observed that in general, adding more data improves the performance or does not decrease it. Even when using good language models and knowledge transfer mechanisms, the best results were attained using data from one or two additional data sets.
Research on Web application injection vulnerabilities detection method based on pattern matching

Attackers can exploit vulnerabilities in web applications to commit malicious acts such as corrupting application functionality and Trojan horse implantation. For injection vulnerabilities in Web applications, existing methods are limited by the variety of programming languages and the difficulty in extracting semantic information to detect complex vulnerabilities. The paper proposes a pattern matching-based method for identifying injection vulnerabilities in Web applications, which transforms vulnerability identification into path matching in graphical databases by modifying the code property graph to enable it to handle more complex inter-functional relationships in Web applications. In this paper, we designed and implemented a prototype system, VulnFinder. By collecting 100 randomly selected Github high star open-source projects as the dataset for performance testing, we found 262 real vulnerabilities and conducted comparison experiments with static scanning tools RIPS and Cobra, VulnFinder far exceeded the comparison tools in terms of vulnerability determination accuracy, and in terms of the dataset VulnFinder was 94% accurate in determining vulnerabilities in the dataset. In scanning large projects, VulnFinder was approximately 21% more efficient than the comparable methodology tool RIPS.
Origin of paratacamite in the Kuqa Basin, Xinjiang, China: Defined by Cu isotope values

Isotopes of Cu in some copper minerals have been used to trace the origin of metallogenic materials. However, little is known about paratacamite, a sedimentary-type Cu mineral whose formation is closely related to brines and evaporites. The Kuqa Basin is filled with a huge thickness of evaporite deposits and is located in the northern margin of the Tarim Basin, Xinjiang, northwestern China. Here, we report a study of paratacamite from the Kuqa Basin and infer its genesis from the mineral assemblage and Cu isotopes of paratacamite. Paratacamite is widely distributed in basin deposits coexisting with saline minerals in fractures and joint planes in fault/fracture zones. Cu isotopic compositions (δ65Cu) of paratacamite range from −0.49‰ to 1.02‰, with a mean of 0.41‰. These values suggest that Cu in the paratacamite originated from Cu-bearing brine that was derived from the leaching of metallic ions from surrounding rocks and migrated along and upward through faults, fractures, and joint planes and ascended to the surface while cooling. On the basis of the paragenetic association of paratacamite with saline minerals in the studied samples, the occurrence of paratacamite is inferred to have been controlled by a crystallisation sequence of saline minerals with accompanying evaporation and concentration of brines from which gypsum, secondary glauberite, paratacamite, and halite were crystallised in turn. Differences in Cu isotopic compositions among the samples from the four studied mineralisation occurrences of Kezier, Yanshuigou, Daxiagu, and Dishui may be due to variation in the processes of Cu enrichment involving brines, such as Cu extraction from different rock layers, the migration and evaporation of Cu-bearing brine, and mineral deposition.
Informatics and Generative Aesthetics

This paper argues that generative music and art techniques have much to tell us about the politics of operations in infrastructural and political contexts, that generative techniques are an unavoidable aspect of informatic operations, and that an artistic and materially-grounded sensibility toward the manipulation of the inherent biases of operations is necessary, if not sufficient, for creating high-stakes automated systems that take an ethics of care as foundational to their becoming. This paper does so through concepts from mid-twentieth century philosopher of technology Gilbert Simondon&rsquo;s On the Mode of Existence of Technical Objects alongside critical readings of a pair of software artifacts&mdash;namely the predictive policing platform Geolitica (formerly PredPol) and the procedurally-generated videogame No Man&rsquo;s Sky. The analysis is supplemented by the author&rsquo;s experience as an experimental electronic musician, media artist and software product developer.
Realization of adaptive system transitions in self-adaptive autonomous robots

The emergence of modern, distributed cyber-physical systems (CPSs), e. g., robotics systems, have opened new lines and directions of research in academia. To meet the demand for an increased system autonomy these CPSs need to continuously adapt to the dynamic environment or context in which they operate. As a result, there is the necessity to design systems that self-adapt and optimize their system state autonomously in response to different run-time context changes. However, making a decision on the optimal adaptation in a changing context is a complex task. In response, in this paper, we proposing a modular analysis and planning approach, which generates the optimal adaptations based on individual sub-decisions. Each sub-decision corresponds to an adaptation module which focuses on a specific parts of the context.
User awareness and defenses against sockpuppet friend invitations in Facebook

Friend relations in Facebook have been used to access private and sensitive user data, post false or abusive information on the timelines and news feeds of victims, and scam and influence the perceptions of victims. In a qualitative study with 35 participants who use Facebook we found that they often confirm invitations received from sockpuppet accounts. Some participants claimed prior exposure to such invitations and exhibited increased awareness. However, in our study several such participants have also confirmed at least one sockpuppet invitation. To take steps toward addressing this problem, in this paper we introduce a new interface to inform, educate and nudge users to inspect their Facebook friend invitations before making a decision, and avoid confirming invitations received from suspicious accounts. We propose a classifier to detect undesirable friend invitations and use it to prioritize a must-see pending friend list. In studies with 145 participants, we found that when compared to a control, Facebook-like interface, our solution reduced confirmed friend invitations from sockpuppet accounts by 42.6 percentage points, reduced blind confirmations of such accounts by 22.6 percentage points, and increased the inspected profiles of such accounts by 54.67 percentage points. We show that when trained on only a few user decisions from these studies, our classifier predicted the user decisions for pending friends with an F1-measure of 96.52%.
DS-CBIoT: a generic framework on data sharing based on consortium blockchain and IoT

The Internet of Things(IoT) is a communication technology which will have a deep influence on data sharing. However, IoT requires security access and data traceability against evil attacks. Blockchain, as the infrastructure of Bitcoin, perfectly meets the need. In this paper, we give out a generic data sharing framework based on consortium blockchain and IoT(CBIoT). Then a formal description and two security characteristics are purposed. Finally, an implementation is presented combing with block.
CosGCN: cosine Gaussian capsule networks for few-shot learning

This paper firstly researches the few-shot learning technology and the capsule routing theories. The iterative consistency mechanism of dynamic capsule routing is combined with the prototype network to design a better generalization performance. And a few-shot image classification model with better robustness for the posture of the sample in the test is proposed. In view of the defects in it, a multi-marginal cosine loss function is proposed for the model. Finally, the CosGCN model is proposed to ensure recognition accuracy and improve the computational efficiency of the model training. Experiments are carried out on the small sample dataset Omniglot and miniImagenet datasets. The results show that the model has better posture robustness.
Towards diversity-tolerant RDF-stores

We propose a novel approach to designing RDF-stores with the goal of improving the consistency and predictability of query performance. When designing these systems, three properties are commonly desired: support for the full range of SPARQL query features (Q), support for widely varying RDF datasets in terms of structuredness and size (S), and high performance (P). We develop the empirical SPQ conjecture which states that it may be impossible to achieve all the three desiderata simultaneously. We present a strong case for its plausibility based on our experimental results. The tradeoffs among the three and design guidelines based on the SPQ conjecture are also discussed.
CIIA

As adversaries continue to develop new attack techniques to undermine organizations' business goals, there is an increase necessity for defenders to understand how a cyber-incident can impact those goals, which has motivated research in mission impact assessment (MIA). This paper presents CIIA (Critical Infrastructure Impact Assessment), an integrated approach for understanding the mission impact of cyber-threats. CIIA was developed to offer a mission-oriented evaluation model to profile the organization, and, upon it, a simulation platform to simulate mission impact of a user-chosen exploited threat. Our experimental evaluation has shown CIIA is successful in generating a relevant report on mission impact for several organizational settings.
Considering similarity and the rating conversion of neighbors on neural collaborative filtering

One of the most popular recommender system techniques is collaborative filtering (CF). Nowadays, many researchers apply a neural network with CF, but few focus on the neighbors' concept of CF. This work needs to consider two major issues: the similarity levels between the neighbors and the target user and the user's rating pattern conversion. Because different neighbors have a different influence on the target user and different users usually have a different rating pattern, the ratings directly utilized by the neighbor's preference pattern may be incorrect for the target user. Under two main issues, we try to accomplish three main ideas of CF's prediction: the similarity between users' levels, the neighbor's rating, and the rating conversion. Thus, we propose three main modules, the rating conversion module, the similarity module, and the prediction module, to solve the two issues mentioned above. The rating conversion module projects the neighbor's rating into the target user's aspect. The similarity module uses the users' attentions to compute similarity levels between users. Finally, these similarity levels and the converted ratings are integrated to perform the prediction. The proposed method is compared with the current CF with friends and latent factor model using two types of datasets: real-world and synthetic datasets. We evaluate N neighbors and all neighbors on real-world datasets to prove the number of neighbor is important. Moreover, the performance of the rating conversion module is also evaluated. The proposed method simulates the full rating datasets and the partial rating dataset to compare the effectiveness of using different types of distribution and dataset size. The experimental results demonstrate that the proposed method effectively outperformed the baselines using ranking evaluation and prediction accuracy on real-world and synthetic datasets. Besides, The effectiveness of using different the number of neighbors depends on the quality of neighbors.
An intelligent blockchain application for emergency medical services

With growing awareness of the importance of digital healthcare systems, an efficient health care system can contribute to a significant part to improve the lives of patients. To improve quality of emergency medical care, we propose in this paper an Intelligent Blockchain application for emergency medical services that use real-time patient data. Specifically, the treatment of real-time data of emergency patients can help the clinical team to deliver health care early. The main underlying idea of our solution is to ensure that data that are gathered are as accurate as possible. To this aim, in this paper, we focus in particular on the detection of false data measurement in emergency medical services. We propose a new Blockchain-based health care monitoring by integrating the Blockchain technique to IoT networks, where the agent-based policy is proposed to detect IoT sensor attacks in the form of false data measurement. The simulation results show that the proposed solution has a low network latency and acceptable sending rate.
Betting on bets

Abstract The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions about the future of AI. Since the column's inception 3 years ago, only a few scientific bets have been collected, despite universal approval around the idea of scientific betting. We hope to widen our reach with an additional first batch of seed bets that are of broad interest to the research community including AI bias, fifth sentence prediction, emotion regulation, big models, and fake news. For detailed guidelines and to place bets, visit sciencebets.org .
Attention based fully convolutional network for video summarization

Video summarization technology extracts key frames or video clips that can most effectively express video content from the original long video, so that the summarized video clips contain the content information that users are most interested in, which is convenient for users to quickly browse and retrieve the video. In this paper, we regard video summarization as a sequence annotation problem. Different from the existing methods using recurrent models, this paper proposes a fully convolutional neural network model combining spatial attention mechanism to solve the video summarization problem. Firstly, the pre-training model is used to extract the cube features of the input video frame, then the cube features are aggregated into the attention vector by the attention mechanism, which is input into the fully convolutional neural network model for binary classification, and then the video summary is generated. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of the proposed method.
Moving object recognition with optical flow-based fusion of thermal and visible videos

Thermal and visible videos can provide complementary information in the moving object detection and recognition. However, most previous approaches focus on the detection and recognition of moving objects from visible videos. In this paper, we present a two-stage approach to moving object recognition by jointly utilizing the thermal and visible videos. In the first stage, we extract the static appearance and the optical flow of moving objects from both sources of videos based on deep networks and generate the bounding box proposals of moving objects. In this stage, two sources of video frames need to be first registered to cover the same scenes with the same resolution. In the second stage, we design a deep network to recognize the categories of the object proposals generated in the first stage and thus obtain the recognition results. Combining the thermal and visible information for recognizing moving objects can improve the performance especially in the low light conditions. To evaluate the proposed approach, we build a thermal-visible video dataset consisting of 200 video pairs. Experimental results demonstrate the effectiveness of the proposed approach.
Design of English Translation Model of Intelligent Recognition Based on Deep Learning

In the context of globalization, the international common language is English and it is more and more widely used. However, there is a shortage of talents with high English proficiency in all industries. According to the demand of the Chinese market, we should pay attention to students’ English language ability. Deep learning is a machine learning method based on feature learning and feature hierarchical structure, which simulates the analysis and learning of the human brain, carries out feature transformation layer by layer, trains a single-layer network each time, and transforms the features of samples in the original space to a new feature space, thus making classification or prediction easier. There are still some defects in the current machine translation (MT) results of online MT, especially the disadvantages of low efficiency and low accuracy of MT when the full-text range uses the server to compare the data of different languages to obtain the grammar and text-related rules among different languages. Therefore, other modern intelligent recognition technologies should be adopted to achieve accurate English MT. Generally speaking, the closer the training data is to the domain of the target text, the higher the quality of sentence alignment and the more sentence pairs, the more helpful it is to learn more accurate translation rules, so as to obtain a more robust translation.
Performance Prediction of Speed Skaters Based on BP Neural Network

To better predict the performance of skaters, a BP neural network-based approach is proposed. With the help of the main component factor analysis method, the performance sample of the athletes was reduced, the traditional high-dimensional data set was changed to the new low-dimensional data set, the size of the model input variables was reduced, and the analysis efficiency was increased and improved. Experimental results show that the larger the iteration step, the lower the prediction error, because more a priori information of sports performance is used through multiple iterations, which improves the prediction accuracy. The error of sports performance prediction using this model is less than that of traditional methods, and the maximum difference is 35.98%. It is proved that this method uses factor analysis and neural network training to cluster and fuse the data information of sports performance, improves the prediction accuracy, and has a good performance in convergence and robustness.
Multi-hop reading comprehension based on reinforcement learning

Multi-hop machine reading comprehension (MRC) across multiple documents poses new challenges over single-hop MRC, because it requires reasoning several times to answer the given questions and being able to show the reasoning path to support its answer as explanation at the same time. In this paper, we propose a new model based on reinforcement learning for multi-hop MRC. Our model mainly consists of two parts: (i) a novel agent that decomposes the multi-hop question into several sub-questions implicitly, (ii) an incorporator incorporating external knowledge to enhance the answering ability of the sub-question answerer. Experimental results show that our model performs better compared to the path-based models and has higher interpretability compared to the graph-based models. In the last section, we discuss the development prospects of our model in the future.
A Sentiment Strength Extraction Method Considering the Effect of Memory for Bicycle Navigation

In cities around the world, new mobility services are both welcomed and challenged by regulators and incumbent operators. For providing a better riding experience to electric bicycle users, we propose an image sentiment strength extraction method considering the effect of memory to realize the safe and comfortable route recommendation. Specifically, we apply a real time sentiment analysis API for obtaining the sentiment labels from the facial expression images as potential feedback. Then, we extract the images of road by using the coordinate on facial expression image, and provide them to users for obtaining the explicit feedback. Furthermore, we also consider the impact of memory on the explicit feedback. Thus, we send each extracted road image to users for evaluation after a period from the time of riding, and we calculate the weights as the impact of memory according to the scores of explicit feedback received at different times. Finally, we combine the weighted explicit feedback and weighted potential feedback to calculate the sentiment strength of the scenery on the route for comfortable bicycle navigation, and verify the influence of memory on sentiment strength.
Metacognitive Strategy Instruction and Mobile-Based Exercises for Adolescent EFL Listeners in Taiwan

Effectiveness studies regarding mobile integration in teaching and learning L2 listening have shown positive results in listening comprehension and in listening strategy development. The present study furthered this line of research by investigating whether metacognitive strategy instruction and mobile-based exercises could improve adolescent EFL learners’ listening abilities and metacognitive awareness. The study recruited three intact classes of 12th graders in a high school in Taiwan to participate in an English listening program for 10 weeks. The participants had a low English proficiency level but were proficient with their smartphones. Each of the three classes had 30 students and was randomly assigned to the metacognitive and mobile experimental group (mobile), the metacognitive experimental group (non-mobile), and the control group. During the 10-week treatment period, both experimental groups of mobile and non-mobile received intensive metacognitive strategy instruction in class, and the mobile group received mobile-based exercises after classes for extra listening practices. The control group received neither. All three groups took a listening proficiency test and completed the metacognitive awareness listening questionnaire before and after the listening program. Some selected students from both experimental groups were interviewed after the program ended. The results did not reveal significant effects of the metacognitive strategy instruction and the mobile-based exercises between the two experimental groups. When compared with the control group, the mobile group yielded a significant effect in listening comprehension and both experimental groups excelled significantly in metacognitive awareness. From their interviews, our adolescent EFL learners approved the listening program focusing on metacognitive strategies and praised the convenience of mobile-based exercises.