Reviewerly/exHarmony huggingface dataset:
MiCrowd: Vision-Based Deep Crowd Counting on MCU

Microcontrollers (MCUs) have been deployed on numerous IoT devices due to their compact sizes and low costs. MCUs are capable of capturing sensor data and processing them. However, due to their low computational power, applications processing sensor data with deep neural networks (DNNs) have been limited. In this paper, we propose MiCrowd, a floating population measurement system with a tiny DNNs running on MCUs since the data have essential value in urban planning and business. Moreover, MiCrowd addresses the following important challenges: (1) privacy issues, (2) communication costs, and (3) extreme resource constraints on MCUs. To tackle those challenges, we designed a lightweight crowd-counting deep neural network, named MiCrowdNet, which enables on-MCU inferences. In addition, our dataset is carefully chosen and completely re-labeled to train MiCrowdNet for counting people from an mobility view. Experiments show the effectiveness of MiCrowdNet and our relabeled dataset for accurate on-device crowd counting.
Attention-Enhanced Lightweight One-Stage Detection Algorithm for Small Objects

The majority of object detection algorithms based on convolutional neural network are focused on larger objects. In order to improve the accuracy and efficiency of small object detection, a novel lightweight object detection algorithm with attention enhancement is proposed in this paper. The network part of the proposed algorithm is based on a single-stage framework and takes MobileNetV3-Large as a backbone. The representation of shallower scale features in the scale fusion module is enhanced by introducing an additional injection path from the backbone and a detection head specially responsible for detecting small objects is added. Instead of pooling operators, dilated convolution with hierarchical aggregation is used to reduce the effect of background pixels on the accuracy of small object locations. To improve the efficacy of merging, the spatial and channel weights of scale features are modified adaptively. Last but not least, to improve the representation of small objects in the training datasets, the Consistent Mixed Cropping method is also proposed. The small labels of standard datasets are expanded with the self-collected samples for the training of the algorithm network. According to the test results and visualization on the 64-Bit Extended (X86-64) platform and embedded Advanced RISC Machine (ARM) platform, we find that the average accuracy (mAP) of the proposed algorithm is 4.6% higher than YOLOv4 algorithm, which achieves better small object detection performance than YOLOv4 algorithm, and the computational complexity is only 12% of YOLOv4 algorithm.
Dynamic Load Balancing in Stream Processing Pipelines Containing Stream-Static Joins

Data stream processing systems are used to continuously run mission-critical applications for real-time monitoring and alerting. These systems require high throughput and low latency to process incoming data streams in real time. However, changes in the distribution of incoming data streams over time can cause partition skew, which is defined as an unequal distribution of data partitions among workers, resulting in sub-optimal processing due to an unbalanced load. This paper presents the first solution designed specifically to address partition skew in the context of joining streaming and static data. Our solution uses state-of-the-art principles to monitor processing load, detect load imbalance, and dynamically redistribute partitions, to achieve optimal load balance. To accomplish this, our solution leverages the collocation of streaming and static data, while considering the processing load of the join and the subsequent stream processing operations. Finally, we present the results of an experimental evaluation, in which we compared the throughput and latency of four stream processing pipelines containing such a join. The results show that our solution achieved significantly higher throughput and lower latency than the competing approaches.
Data Rights Confirmation Scheme Based on Auditable Ciphertext CP-ABE in the Cloud Storage Environment

Advances in information technology have made data accessible anytime and anywhere. Currently, data confirmation is a popular area of research. Many current approaches to data confirmation rely on submitting certificates of ownership, embedding digital watermarks, or using blockchain. However, none of these approaches can avoid exposing source data to third parties that are not fully trusted. To address this issue, this paper proposes a new data confirmation method based on ciphertext policy attribute-based encryption (CP-ABE), which is widely used in cloud storage environments. The unique identifier of the data owner is encrypted by Paillier encryption and embedded into the ciphertext, so that the ownership corresponding to the plaintext is converted to the ownership corresponding to the ciphertext. During the entire confirmation process, third-party organizations cannot access the source data, reducing the risk of source data leakage. Finally, the feasibility of the scheme is proved by security proof and experiment comparison.
Server-side Cross-site Scripting Detection Powered by HTML Semantic Parsing Inspired by XSS Auditor

Cross-site Scripting attacks have been a perennial threat to web applications for many years. Conventional practices to prevent cross-site scripting attacks revolve around secure programming and client-side prevention techniques. However, client-side preventions are still prone to bypasses as the inspection is done on the user’s browser, so an adversary can alter the inspection algorithm to come up with the bypasses or even manipulate the victim to turn off the security measures. This decreases the effectiveness of the protection and leads to many web applications are still vulnerable to cross-site scripting attacks. We believe that XSS Auditor, which was pre-installed in Google Chrome browser for more than 9 years, is a great approach in combating and preventing XSS attacks. Hence, in this paper, we proposed a novel approach to thoroughly identify two types of cross-site scripting attacks through server-side filter implementation. Our proposed approach follows the original XSS Auditor mechanism implemented in Google Chrome. However, instead of placing the detection system on the client side, we design a detection mechanism that checks HTTP requests and responses as well as database responses for possible XSS attacks from the server side. From 500 payloads used to evaluate the proposed method, 442 payloads were classified correctly, thus showing that the proposed method was able to reach 88.4% accuracy. This work showed that the proposed approach is very promising in protecting users from devastating Cross-site Scripting attacks.
Argumentation as a Speech Act: Two Levels of Analysis

Abstract Following and extending Searle’s speech act theory, both Pragma-Dialectics and the Linguistic Normative Model of Argumentation characterize argumentation as an illocutionary act. In these models, the successful performance of an illocutionary act of arguing depends on the securing of uptake, an illocutionary effect that, according to the Searlean account, characterizes the successful performance of any illocutionary act. However, in my view, there is another kind of illocutionary effect involved in the successful performance of an illocutionary act of arguing, which affects both the speaker’s and the hearer’s set of rights, obligations, and entitlements. In order to give an account of this second type of effect, I will argue that it is necessary to distinguish two levels in the analysis of the illocutionary act of arguing . The first one is related to the illocutionary effect of securing of uptake and thus to the speech act performed by the speaker, while the second one allows us to account for the changes produced by the performance of the illocutionary act of arguing in the deontic modal competence of both the speaker and the hearer.
The Analysis of Characteristics of Each Cluster by Teacher Efficacy and Enthusiasm

This study is to derive cluster types according to teacher efficacy and enthusiasm of high school teachers by using the 9th year(2020) Gyeonggi Education Panel Study(GEPS) data and to investigate the characteristics of each cluster group. As a result of cluster analysis, the teacher group was categorized into four groups (Upper, Middle, Inactive, and Lower). Next, as the results of the ANOVA(F-test) to analyze the characteristics of each group, ‘the lower group’ was the lowest mean score in the ‘teaching· learning’ variables, and ‘the inactive group’ was the lowest mean score in the ‘school organization’ variables. In addition, as the results of multinomial logistic regression analysis based on ‘the middle group' to explore the main variables that can predict each group, the 'teaching·learning' variables are shown as relatively better predictors for each group than the 'school organization' variables. Based on these research results, implications for improving school organizational culture, teacher management, and teacher training were discussed in the future.
Deep knowledge tracing with learning curves

Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior performance over traditional approaches. However, they ignore the explicit modeling of the learning curve theory, which generally says that more practices on the same knowledge concept enhance one's mastery level of the concept. Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model and a Capsule-Enhanced CAKT (CECAKT) model to enable learning curve modeling. In particular, when predicting a student's response to the next question associated with a specific knowledge concept, CAKT uses a module built with three-dimensional convolutional neural networks to learn the student's recent experience on that concept, and CECAKT improves CAKT by replacing the global average pooling layer with capsule networks to prevent information loss. Moreover, the two models employ LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional/capsule module. As such, the two models can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. Experimental results on four real-life datasets show that CAKT and CECAKT both achieve better performance compared to existing deep KT models.
Similar image matching via global topology consensus

Abstract Recovering three-dimensional structure from images is one of the important researches in computer vision. The quality of feature matching is one of the keys to obtaining more accurate results. However, as different objects or different surfaces of objects have similar images with the same elements and different typography, the camera pose estimation will be wrong and the task will fail. This paper proposes a new mismatch elimination algorithm based on global topology consistency. We first formulate the matching task as a mathematical model based on the global constraints, then convert the feature matching into grid matching, calculate the confidence of the grids according to the changes in the angle and displacement between correspondence grid vectors, and remove the mismatches with low confidence. The experiments have demonstrated that our proposed method performs better than the state-of-the-art feature matching methods to accomplish outlier match rejection in the task of similar image matching and could be helped to obtain the correct camera pose to reconstruct more complete and more accurate object models.
Language Game, Asserting and Speech Acts A New View on Uptake in Speech Acts

Hornsby and Langton argued the viewpoint of “illocutionary disablement” in pornography based on Austin’s Speech Act theory. They called this silencing and regarded it as a violation of free speech. Sharma has presented a profound criticism of the former study and issued her own opinions based mainly on the intentionalist point of view, which is inaccurate and not satisfactory enough. To further the discussion, this paper argues that the reason for her defects locates in the intentional fundament. This paper creatively puts speech acts into the framework of Wittgenstein’s Language Game Theory and Brandom’s Asserting Theory, namely, the social view. This paper finally reaches a conclusion that the basic essence of speech acts is conventionality based on language game and the social web created by asserting. The intention is the secondary essence, which is highly related to securing uptake when it comes to further taxonomy. On this new framework, this paper also put forward the questions of the relationships between speech act verbs and speech acts, the further taxonomy of non-conventional speech acts for future discussions.
Analyze on Why Freedom of Speech Should be Restricted from the Perspective of Language Game

As one of the most important modern western philosophers, Wittgenstein's language game theory laid the foundation for the study of pragmatics, and at the same time made the theory of "language game" widely studied in the field of linguistics. Linguistics is relatively active in the fields of mass media and social information dissemination, and the impact of language games on mass media and information dissemination has also achieved certain results in research. Combined with the current era, the rapid development of Internet technology has also led to the development of mass media and information dissemination effectiveness, opened up an unprecedented and convenient way for citizens to exercise their right to freedom of speech, and gave birth to the field of cyberspace that coexists with real space. The Internet platform allows more people to participate in the activities of expressing and disseminating speech, promoting the improvement of social norms, and solving social problems, but at the same time, online language violence and rumors, and defamation are also problems that cannot be underestimated, threatening national and social security. Based on Wittgenstein's "language game" theory, this article analyzes the spread and influence of language game theory, especially deeply derogatory words, on historical and contemporary social speech, and responds to why freedom of speech should be restricted.
Analysis on The Good Doctor Season 1 from the Perspective of Cooperative Principle

In 1967, the American language philosopher Grice proposed the cooperative principle, which provided a new theoretical framework for conversation analysis. In the following decades, the cooperative principle has been a trending topic of discussion in linguistics and continuously developed. Leech’s politeness principle and Wilson and Sperber’s relative theory have improved the cooperation principle from different aspects. The cooperative principle has been widely applied in the analysis of conversations in films, dramas, and literature, which contributes to a holistic understanding of characterization and dramatic conflicts. In this paper, doctor-patient dialogues in The Good Doctor Season 1 are analyzed from the theoretical perspective of the cooperative principle. It can be discerned that healthcare workers should comply with the maxim of quality and quantity, to no little extent, when communicating with patients. However, in different contexts, healthcare workers sometimes choose to deliberately violate the maxim of relation and manner in order to achieve better communication effects, such as helping patients understand their conditions or relieving patients’ tension. This paper intends to provide effective suggestions for real-life doctor-patient communication by applying the cooperative principle to the corpus analysis in The Good Doctor Season 1, so as to improve the communication efficiency of both parties and create a harmonious doctor-patient relationship.
Asymmetry of Online Speech: The Danger for Speaker and Language Use

Network society possesses more influence towards people’s lives presently. As a consequence, the caring for uptake is growing within speech acts in online contexts. From the realistic world to the virtual one, the linguistic boundary is becoming broader, so as the boundaries of people themselves. The information age has already changed the cognition about human beings, which is the essence of people is informational fluid that can depart from the body and present an ambiguous status on the network. Causing by the rise of the position of the uptake, a speaker is more likely to fall into an asymmetrical interlocution when exposed to the Internet. This conversational inequality would change the way people speak, and furtherly influence their ideology. The dangers of online speech are therefore unsealed, including the sluggishness and rigidification of language use as well as the conversion within the speakers’ minds, whose details will be discussed in this paper.
Some Remarks on Semantics and Expressiveness of the Sentential Calculus with Identity

Suszko's Sentential Calculus with Identity SCI results from classical propositional calculus CPC by adding a new connective $\equiv$ and axioms for identity $\varphi\equiv\psi$ (which we interpret here as `propositional identity'). We reformulate the original semantics of SCI in terms of Boolean prealgebras establishing a connection to `hyperintensional semantics'. Furthermore, we define a general framework of dualities between certain SCI-theories and Lewis-style modal systems in the vicinity of S3. Suszko's original approach to two SCI-theories corresponding to S4 and S5 can be formulated as a special case. All these dualities rely particularly on the fact that Lewis' `strict equivalence' is axiomatized by the SCI-principles of `propositional identity'.
Enhancing motion visual cues for self-supervised video representation learning

Building the general feature from unlabeled videos is the core of self-supervised video representation learning. However, recent research on video representation focuses on static visual pixel information, which makes these models unable to capture the dynamics in the temporal dimension. In order to solve the aforementioned issue and improve the generalization of the model, we propose an enhanced motion visual cue (EMVC) method for self-supervised video representation learning to reduce the background bias and increase the motion information. Our EMVC includes a background replacing module and a foreground fixing module that leverages the foreground and background of the original video sequence to make the background of same-action videos less identical and their motion cues more distinct. Experimental results show that the proposed method effectively reduces the biases in the background and significantly improves the video’s ability to comprehend motion information, leading to an increase in recognition accuracy. Specifically, the EMVC method achieved an accuracy of 84.6%, 53.1%, and 68.3% on the UCF101, HMDB51, and Diving48 datasets, respectively, outperforming the existing algorithms. Additionally, significant improvements were obtained in the video retrieval task on benchmark datasets, with an average improvement of over 14.6%.
Non-communicating Decentralized Multi-robot Collision Avoidance in Grid Graph Workspace based on Dueling Double Deep Q-Network

Abstract This paper designs a motion rule suitable for grid environment and proposes a multi-robot autonomous obstacle avoidance method based on deep reinforcement learning. The training and validation of the method are done under the Stage simulation platform based on ROS operating system. During the training process, the robot uses Lidar to obtain the surrounding state information and generates actions based on the state information to obtain rewards, and the robot is guided by the rewards to optimize the strategy. Based on the D3QN algorithm, a new reward function is designed, a proximity penalty is introduced to reduce the collision between robots, a distance reward is added to guide the robot to complete the task, a step reward is added to improve the efficiency of the robot to complete the task, and an illegal action penalty is added to avoid the robot to choose an illegal action; the input is 5 frames of Lidar data, and in the network structure, the agent can better learn the correlation between the data by introducing Long Short Term Memory(LSTM) layer, and introducing Convolutional Block Attention Module(CBAM), a hybrid attention mechanism to allow the robot to pay more attention to the information of the surrounding robots. By designing experiments, we demonstrate that the learned strategy can effectively guide the robot through obstacle avoidance and complete the task.
EGAsubmitter: A software to automate submission of nucleic acid sequencing data to the European Genome-phenome Archive

Making raw data available to the research community is one of the pillars of Findability, Accessibility, Interoperability, and Reuse (FAIR) research. However, the submission of raw data to public databases still involves many manually operated procedures that are intrinsically time-consuming and error-prone, which raises potential reliability issues for both the data themselves and the ensuing metadata. For example, submitting sequencing data to the European Genome-phenome Archive (EGA) is estimated to take 1 month overall, and mainly relies on a web interface for metadata management that requires manual completion of forms and the upload of several comma separated values (CSV) files, which are not structured from a formal point of view. To tackle these limitations, here we present EGAsubmitter, a Snakemake-based pipeline that guides the user across all the submission steps, ranging from files encryption and upload, to metadata submission. EGASubmitter is expected to streamline the automated submission of sequencing data to EGA, minimizing user errors and ensuring higher end product fidelity.
How Metaphor Strategies Work in Particular Internet Context Based on Grice's Non-natural Meaning Theory

The Internet's network communication mode has been strikingly different from more conventional forms ever since it was conceived. Memes, which can only be fully comprehended once a certain network group has assimilated people's views, are just one example of how network communication is spawning a new revolution in the meaning of language in particular senses. Furthermore, if people are uncomfortable using particular expressions online, other words will emerge to take their place. They could worry that they'll be bullied or harassed if they try to voice an alternative viewpoint. In this research, the author applies Grice's theory of non-natural meaning to analyse the potential impact of a few popular Chinese internet phrases and memes on the average Chinese person. It will be determined that the widespread use of non-natural meanings has a negative impact on the network environment and makes people playing more sentimental roles in passing along information. Because of this, a different method of network governance is required.