Reviewerly/exHarmony huggingface dataset:
Caption Suggestions Through Facial Expression Analysis

Posting photographs online has become an essential aspect of consumer’s life in recent years because of the rapid development of social networking sites searching for the right caption for posting their picture could be troublesome work and few users spend hours together in search of a good caption. Thus, Image captioning gradually attracted the attention of many people and became an interesting and arduous task. Image captioning automatically generates captions according to the emotions observed in the user’s face. Our project aims to suggest captions for the picture by detecting the emotion in the user’s face to simplify their task. The proposed system uses a machine-learning classification algorithm to detect the emotion in user’s face when they post an image on Instagram. For example, if the user posts a happy picture then our Image Captioner generates all captions related to happiness. Through this, people don’t have to spend hours of their precious time searching for a suitable caption for their image.
On robustness and local differential privacy

It is of soaring demand to develop statistical analysis tools that are robust against contamination as well as preserving individual data owners’ privacy. In spite of the fact that both topics host a rich body of literature, to the best of our knowledge, we are the first to systematically study the connections between the optimality under Huber’s contamination model and the local differential privacy (LDP) constraints. In this paper, we start with a general minimax lower bound result, which disentangles the costs of being robust against Huber contamination and preserving LDP. We further study four concrete examples: a two-point testing problem, a potentially diverging mean estimation problem, a nonparametric density estimation problem and a univariate median estimation problem. For each problem, we demonstrate procedures that are optimal in the presence of both contamination and LDP constraints, comment on the connections with the state-of-the-art methods that are only studied under either contamination or privacy constraints, and unveil the connections between robustness and LDP via partially answering whether LDP procedures are robust and whether robust procedures can be efficiently privatised. Overall, our work showcases a promising prospect of joint study for robustness and local differential privacy.
Realizing credible remote agricultural auditing with trusted video technology

Abstract The conventional approaches of auditing agricultural commodities from the production and transportation level to the retailers and consumers often get obstructed by the lack of human resources, delayed response, and high frequency of location updates—resulting in poor quality and safety compliance. Its digital transformation, known as remote auditing, could address these limitations to a greater extent; however, it is still subject to diverse cyberattacks, including tampering with the video streams provided for verification. Since a detailed and concurrent forensic examination of videos during remote auditing significantly increases the computational burden on the network and infrastructure, alternative or complementary solutions should be developed. This paper outlines the technical feasibility of applying digital signatures over live video streams as a way to authenticate the source during remote auditing and thus contributes to limiting the scope of potential cyber threats. It proposes design options for realizing the authentication process with trusted computing technologies at different phases, from signing the videos to transmitting them through unreliable networks. A reference prototype is also developed based on the proposed end-to-end design to quantify the performance of trusted remote agricultural auditing in terms of the frame signing time, attack resistance, and resource overhead.
Child-Sum EATree-LSTMs: enhanced attentive Child-Sum Tree-LSTMs for biomedical event extraction

Tree-structured neural networks have shown promise in extracting lexical representations of sentence syntactic structures, particularly in the detection of event triggers using recursive neural networks.In this study, we introduce an attention mechanism into Child-Sum Tree-LSTMs for the detection of biomedical event triggers. We incorporate previous researches on assigning attention weights to adjacent nodes and integrate this mechanism into Child-Sum Tree-LSTMs to improve the detection of event trigger words. We also address a limitation of shallow syntactic dependencies in Child-Sum Tree-LSTMs by integrating deep syntactic dependencies to enhance the effect of the attention mechanism.Our proposed model, which integrates an enhanced attention mechanism into Tree-LSTM, shows the best performance for the MLEE and BioNLP'09 datasets. Moreover, our model outperforms almost all complex event categories for the BioNLP'09/11/13 test set.We evaluate the performance of our proposed model with the MLEE and BioNLP datasets and demonstrate the advantage of an enhanced attention mechanism in detecting biomedical event trigger words.
Virtual Reality Health Education to Prevent Musculoskeletal Disorders and Chronic Low Back Pain in Formal and Informal Caregivers.

Formal and informal caregivers are suffering from musculoskeletal disorders and low back pain which develops in practice. Previous research has suggested that health education can help to prevent musculoskeletal disorders and low back pain in caregivers. With the ability to simulate real-world scenarios, healthcare education is experiencing rapid growth in the use of immersive technologies such as Virtual Reality, aiming to enhance lifelong learning for caregivers, with promising results. However, the creation of such technologies has not been well documented, which forces educators to face the same setbacks during this development process. Using a user-centred design approach, which involved 14 medical experts and computer scientists, we designed a mobile Virtual Reality application to enhance learning and reduce work attributions for caregivers. Then we evaluated the system with a total of 30 formal and informal caregivers, documenting that Virtual Reality can be an effective solution for lifelong learning. Through this paper, we explain the process and analysis we run to identify how to create an effective Virtual Reality learning system.
Profiling Hyperscale Big Data Processing

Computing demand continues to grow exponentially, largely driven by "big data" processing on hyperscale data stores. At the same time, the slowdown in Moore's law is leading the industry to embrace custom computing in large-scale systems. Taken together, these trends motivate the need to characterize live production traffic on these large data processing platforms and understand the opportunity of acceleration at scale.
PROLEG: Practical Legal Reasoning System

This paper introduces a legal knowledge representation language, PROLEG. PROLEG rules are general rules in the form of Horn clauses and special meta-prediate expressing exceptions. Exceptions are introduced to express negative information in stead of “negation as failure”. It is because reasoning pattern of general rules and exceptions fits lawyers’ reasoning and therefore lawyers understand PROLEG easily. We firstly give the definition of syntax and semantics of PROLEG and show an application for legal reasoning.
Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics

Over the last decade, the use of Automatic Speaker Verification (ASV) systems has become increasingly widespread in response to the growing need for secure and efficient identity verification methods. The voice data encompasses a wealth of personal information, which includes but is not limited to gender, age, health condition, stress levels, and geographical and socio-cultural origins. These attributes, known as soft biometrics, are private and the user may wish to keep them confidential. However, with the advancement of machine learning algorithms, soft biometrics can be inferred automatically, creating the potential for unauthorized use. As such, it is crucial to ensure the protection of these personal data that are inherent within the voice while retaining the utility of identity recognition. In this paper, we present an adversarial Auto-Encoder-based approach to hide gender-related information in speaker embeddings, while preserving their effectiveness for speaker verification. We use an adversarial procedure against a gender classifier and incorporate a layer based on the Laplace mechanism into the Auto-Encoder architecture. This layer adds Laplace noise for more robust gender concealment and ensures differential privacy guarantees during inference for the output speaker embeddings. Experiments conducted on the VoxCeleb dataset demonstrate that speaker verification tasks can be effectively carried out while concealing speaker gender and ensuring differential privacy guarantees; moreover, the intensity of the Laplace noise can be tuned to select the desired trade-off between privacy and utility.
Server-Client Collaborative Distillation for Federated Reinforcement Learning

Federated Learning (FL) learns a global model in a distributional manner, which does not require local clients to share private data. Such merit has drawn lots of attention in the interaction scenarios, where Federated Reinforcement Learning (FRL) emerges as a cross-field research direction focusing on the robust training of agents. Different from FL, the heterogeneity problem in FRL is more challenging because the data depends on the policy of agents and the environment dynamics. FRL learns to interact under the non-stationary environment feedback, while the typical FL methods aim at handling the constant data heterogeneity. In this article, we are among the first attempts to analyze the heterogeneity problem in FRL and propose an off-policy FRL framework. Specifically, a student–teacher–student model learning and fusion method, termed as Server-Client Collaborative Distillation (SCCD), is introduced. Unlike the traditional FL, we distill all local models on the server side for model fusion. To reduce the variance of the training, a local distillation is also conducted every time the agent receives the global model. Experimentally, we compare SCCD with a range of straightforward combinations between FL methods and RL. The results demonstrate that SCCD has a superior performance in four classical continuous control tasks with non-IID environments.
3-D Reconstruction Method for a Multiview System Based on Global Consistency

Structured light-based micro three-dimensional (3D) imaging technology is widely used in industrial applications. Using a well-calibrated camera and a projector, it is easy to obtain 3D surface topography of objects. However, single camera-projector pairs suffer from problems caused by shadows and their accuracy is limited due to the restricted field of view. To overcome these issues, this article proposes a multi-projector vision system consisting of a central camera and four surrounding projectors. The global geometry constraint scheme is applied to ensure consistency and stability across multiple projectors, which is achieved by board-to-board pose optimization. In addition, instead of directly using the iso-phase fringe line, geometric plane constraints are used in the calibration process to estimate corresponding projector pixel coordinates. Finally, a triangulation-based 3D reconstruction and data fusion approach is proposed to obtain high precision and shadow-reduced 3D surface topography. Experimental results are presented and discussed that demonstrate the good consistency and measurement accuracy of the proposed approach. The global geometry constraint scheme allows relative standard deviation and root-mean-square error in the measurements to be significantly reduced.
Unsupervised Domain-Adaptive Object Detection via Localization Regression Alignment

Unsupervised domain-adaptive object detection uses labeled source domain data and unlabeled target domain data to alleviate the domain shift and reduce the dependence on the target domain data labels. For object detection, the features responsible for classification and localization are different. However, the existing methods basically only consider classification alignment, which is not conducive to cross-domain localization. To address this issue, in this article, we focus on the alignment of localization regression in domain-adaptive object detection and propose a novel localization regression alignment (LRA) method. The idea is that the domain-adaptive localization regression problem can be transformed into a general domain-adaptive classification problem first, and then adversarial learning is applied to the converted classification problem. Specifically, LRA first discretizes the continuous regression space, and the discrete regression intervals are treated as bins. Then, a novel binwise alignment (BA) strategy is proposed through adversarial learning. BA can further contribute to the overall cross-domain feature alignment for object detection. Extensive experiments are conducted on different detectors in various scenarios, and the state-of-the-art performance is achieved; these results demonstrate the effectiveness of our method. The code will be available at: https://github.com/zqpiao/LRA.
Progressive Histogram Modification for JPEG Reversible Data Hiding

With the development of social network, a large number of private JPEG images are stored in social cloud platform. Correspondingly, the platform embeds user ID or authentication labels to manage these privacy images, preventing them from being arbitrarily accessed or tampered by illegal persons. However, data embedding in JPEG domain inevitably produces irreversible modifications to DCT coefficients, thus resulting in obvious or even serious distortion in the host JPEG images. To address this problem, this paper proposes an efficient JPEG reversible data hiding (RDH) method by constructing progressive two-dimensional histogram mappings. We firstly design distortion function to calculate the cost of each DCT frequency band, and then sort them to build histogram mapping containing a series of coefficient pairs. Subsequently, a progressive mapping mechanism is introduced to maintain most of AC coefficients unchanged. According to the given capacity, this mechanism can adaptively generate an optimum two-dimensional histogram mapping to embed secret messages. Our scheme can achieve an effective balance among embedding capacity, visual quality of the marked image and file size expansion, while keeping high cost-performance complexity. Extensive experiments demonstrate that our method outperforms existing JPEG RDH schemes in terms of visual quality and file size increment of the marked image, and provides an efficient solution for the confidentiality and security access problem of sensitive private image in cloud environment.
Deformable channel non‐local network for crowd counting

Abstract Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models.
Enhancement of dynamical entanglement in a dispersive two-mode Jaynes–Cummings model via superposition of photon-added pair coherent state

Abstract In this paper, we introduce a new non-Gaussian state called superposition of photon-added pair coherent state (SPAPCS) by superposition addition of photons to two modes of a pair coherent state (PCS). It is demonstrated that the SPAPCS has an enhanced non-Gaussian property by testing its Wigner function when increasing the number of photons added to two modes of the PCS. We use the field as a SPAPCS to study a dynamical entanglement in a dispersive two-mode Jaynes–Cummings model in dissipation atom-field interaction. We detect the dynamical entanglement degrees of the global system and subsystems by using the linear entropy criteria. The obtained results show the dynamical entanglement degrees can be enhanced by the phase damping effect as well as the superposition adding photons to two modes of the field in SPAPCS.
Research on Fuzzy Comprehensive Evaluation of the Quality of Teaching Work in Teaching and Research Offices

The teaching and research department is a grassroots unit responsible for teaching tasks in universities, and the quality of its work is directly related to the level of teaching in universities and the quality of talent cultivation.Establishing an evaluation mechanism and effective evaluation methods for the quality of teaching work in the teaching and research department will undoubtedly play a significant role in strengthening and improving the teaching management of universities, promoting the construction of disciplines and courses, and improving the teaching ability and teaching level of teachers.Due to many factors that affect the quality of teaching work in the teaching and research department, the selection of evaluation indicators has a certain degree of ambiguity.At the same time, the proportion (weight and score) of each indicator in teaching work needs to be manually specified.Therefore, the fuzzy comprehensive evaluation method should be used to evaluate the quality of teaching work in the teaching and research department.
Climbing up the ladder of abstraction: how to span the boundaries of knowledge space in the online knowledge market?

Abstract The challenge of raising a creative question exists in recombining different categories of knowledge. However, the impact of recombination remains controversial. Drawing on the theories of knowledge recombination and category-spanning, we claim that the impact of knowledge spanning on the appeal of questions is contingent upon questions’ knowledge hierarchy in the knowledge space. Using word embedding models and network analysis to quantify knowledge spanning and knowledge hierarchy respectively, we test our hypotheses with the data collected from a large online knowledge market ( N = 463,545). Knowledge spanning has an inverted U-shaped influence on the appeal of questions: the appeal of questions increases up to a threshold, after which point the positive effect reverses. However, with the increase in knowledge hierarchy, the inverted U-shape is weakened and disappears quickly. We fill the research gap by conceptualizing question-asking as knowledge-spanning and highlighting the theoretical underpinnings of knowledge hierarchy. The theoretical and practical implications for future research on knowledge recombination are discussed.
Competencias digitales docentes en la educación superior. Un análisis bibliométrico

In recent times, digital literacy and the effective use of technological tools in the educational process have become a challenge for Higher Education institutions. The 21st century teacher must have acquired digital competences that favour productive and quality pedagogical processes, through the inclusion of technologies in their classes and the use of these tools to promote critical thinking, the functionality of learning and collaborative work. The aim of this research was to analyse the scientific productivity related to the acquisition of digital competences in Higher Education teaching staff in relation to the use of technological resources in the teaching space of the different academic disciplines. A total of 86 scientific articles published between 2006 and 2022 were reviewed. The results reveal a growing productivity of studies in recent times, which coincides with the period of confinement due to the COVID-19 pandemic, which gave greater relevance to digital tools. It concludes with the importance of digital teacher training to respond to the needs of students in today's knowledge society with innovative and effective pedagogical proposals.
Automatic Educational Question Generation with Difficulty Level Controls

We consider the task of automatically generating math word problems (MWPs) of various difficulties that meet the needs of teachers in teaching and testing students in corresponding educational stages. Existing methods fail to produce high-quality problems while allowing the teacher control over the problem difficulty level. In this work, we introduce a controllable MWP generation pipeline that samples from an energy language model with various expert model components for realizing the target attributes. We control the difficulty of the resulting MWPs from mathematical and linguistic aspects by imposing constraints on equations, vocabulary, and topics. We also use other control attributes including fluency and distance to the conditioning sequence to manage language quality and creativity. Experiments and evaluation results demonstrate our approach improves upon the baselines in generating solvable, well-formed, and diverse MWPs of controlled difficulty levels. Lastly, we solicit feedback from various math educators who approve the effectiveness of our system for their MWP design processes. They suggest our outputs align with the expectations of problem designers showing a possibility of using such problem generators in real-life educational scenarios. Our code and data are available on request.
A novel multimodal multi-objective optimization algorithm for multi-robot task allocation

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints.