Reviewerly/exHarmony huggingface dataset:
Metaverse ve Psikiyatri: Bir Gözden Geçirme

Today, with the development of technology, the concept of the virtual world is gaining more and more importance. "Metaverse", which has become popular in recent years, aims to provide an environment where people can interact, do business, have fun and even live in the digital world. Metaverse, which has become increasingly popular in recent years, offers many different uses in the field of mental health. However, we do not yet have enough information about the effects of this technology on mental health. The effect of Metaverse on mental health has been the subject of many researches that it can be used in areas such as the treatment of psychological disorders, stress reduction, self-confidence, and development of social interaction skills. The biggest advantage of using Metaverse for psychiatric treatment is that the virtual world is separated from the real world. Therefore, the stress, anxiety and other emotional reactions experienced during treatment have no real-life consequences. In addition, the therapist is easier to access in the virtual world and can help patients adapt to treatment more easily. However, the use of Metaverse for psychiatric treatment also has some disadvantages. For example, this method of treatment may be an unrealistic experience for some patients and not effective enough for others. Also, this treatment modality has limited accessibility as it requires high cost equipment and technology. In particular, there has not been enough research on the relationship between the use of Metaverse in the diagnosis and treatment of psychiatric diseases. More research is needed on the effect of Metaverse on mental health. In particular, more studies are needed on its long-term effects and effects on different people. In this review, studies investigating the potential relationship between Metaverse technology and psychiatry and how this technology can be used in the psychiatric field are reviewed.
Modified Caesar Cipher and Card Deck Shuffle Rearrangement Algorithm for Image Encryption

In today's social networking era, information security breaches are at an all-time high, especially when it comes to texts and images, which should come as no surprise. Integrity is therefore crucial for information sharing over an unprotected connection. As a result, image/text encryption is a crucial preprocessing step in today's information transmission. Sensitive information may be sent via image encryption, which facilitates a variety of procedures. Here the proposed method is a combination of the Modified Caesar Cipher and the Card Deck Shuffle Algorithm for image encryption. The Card Deck Shuffle Algorithm rearranges the pixels from the result of the Modified Caesar approach, which encrypts the pixel value of each image. The method employs an n + 8 variable bit key (n is frequently more than 18 in real-world applications), which needs more than 226 brute force attacks to be successful. The method described here may be used to secure images in a variety of multimedia applications.
Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions

Models for affective text generation have shown a remarkable progress, but they commonly rely only on basic emotion theories or valance/arousal values as conditions. This is appropriate when the goal is to create explicit emotion statements (“The kid is happy.”). Emotions are, however, commonly communicated implicitly. For instance, the emotional interpretation of an event (“Their dog died.”) does often not require an explicit emotion statement. In psychology, appraisal theories explain the link between a cognitive evaluation of an event and the potentially developed emotion. They put the assessment of the situation on the spot, for instance regarding the own control or the responsibility for what happens. We hypothesize and subsequently show that including appraisal variables as conditions in a generation framework comes with two advantages. (1) The generation model is informed in greater detail about what makes a specific emotion and what properties it has. This leads to text generation that better fulfills the condition. (2) The variables of appraisal allow a user to perform a more fine-grained control of the generated text, by stating properties of a situation instead of only providing the emotion category. Our Bart and T5-based experiments with 7 emotions (Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame), and 7 appraisals (Attention, Responsibility, Control, Circumstance, Pleasantness, Effort, Certainty) show that (1) adding appraisals during training improves the accurateness of the generated texts by 10 pp in F1. Further, (2) the texts with appraisal variables are longer and contain more details. This exemplifies the greater control for users.
The Difficulty of Novelty Detection and Adaptation in Physical Environments

Detecting and adapting to novel situations is a major challenge for AI systems that operate in open-world environments. One reason for this challenge is due to the diverse range of forms that novelties can take. To accurately evaluate an AI system's ability to detect and adapt to novelties, it is crucial to investigate and formalize the difficulty of different novelty types. In this paper, we propose a method for quantifying the difficulty of novelty detection and novelty adaptation in open-world physical environments, considering factors such as the appearance and location of objects, as well as the actions required by the agent. We implement several difficulty measures using a combination of qualitative spatial relations, learning algorithms, and statistical distance measures. To demonstrate an application of our approach, we apply our difficulty measures to novelties in the popular physics simulation game Angry Birds. We invite researchers to incorporate the proposed novelty difficulty measures when evaluating AI systems to gain a better understanding of their limitations and identify areas for future improvement.
CySpider: A Neural Semantic Parsing Corpus with Baseline Models for Property Graphs

Enterprise knowledge graphs are gaining increasing popularity in industrial applications, with a pressing demand for natural language interfaces to support non-technical end-users. For natural language queries to relational databases, the neural semantic parsing task Text-to-SQL achieves strong performance in translating text inputs to SQL queries. However, very few public corpora are available for the training of neural semantic parsing models that convert textual queries to graph query languages. In this research, we develop a generic SQL2Cypher algorithm that can map a SQL query to a set of Cypher clauses, where Cypher is a query language used by a popular property graph database Neo4j. The converted Cypher statement is then combined with the original natural language query to create a parallel corpus that enables end-to-end training of neural semantic parsing models for Text-to-Cypher. To evaluate the dataset quality, we construct a corresponding graph database to obtain execution accuracy. In addition, the Text-to-Cypher corpus features four transformer-based baseline models. The availability of such corpus and baseline models is critical in developing and benchmarking new machine learning methods in advancing natural language interfaces for fact retrieval from large graph-based knowledge repositories. The source code and dataset are available at github( https://github.com/22842219/SemanticParser4Graph ).
Coping with Data Distribution Shifts: XAI-Based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction

Adapting to data distribution shifts after training remains a significant challenge within the realm of Artificial Intelligence. This paper presents a refined approach, superior to Automated Hyper Parameter Tuning methods, that effectively detects and learns from such shifts to improve the efficacy of prediction models. By integrating Explainable AI (XAI) techniques into adaptive learning with SHAP clustering, we generate interpretable model explanations and use these insights for adaptive refinement. Our three-stage process: (1) SHAP value generation for the model explanation, (2) clustering these values for pattern identification, and (3) model refinement based on the derived SHAP cluster characteristics, mitigates overfitting and ensures robust data shift handling. We evaluate our method on a comprehensive dataset comprising energy consumption records of buildings, as well as two additional datasets, to assess the transferability of our approach to other domains, regression, and classification problems. Our experiments highlight that our method not only improves predictive performance in both task types but also delivers interpretable model explanations, offering significant value in dealing with the challenges of data distribution shifts in AI.
Lateral AI: Simulating Diversity in Virtual Communities

In this paper, we present Lateral AI that offers a diverse and multi-dimensional world experience. It makes use of semi-automated prompt engineering on top of GPT3.5. The coupling with named entity recognition and text summarization enables creation of a diversity of AI personas and a multiplicity of requests. The features of Lateral AI, such as creation of custom AI personas, prioritisation of user-embedded knowledge in those personas and follow-up requests, enable users to co-create with AI. Users can contribute certain information and perspectives to the application if a Large Language Model does not have access to it. Lateral AI makes the user an active component of the integrated system rather than a mere AI consumer. We demonstrate use of Lateral AI to generate a range of diverse responses and illustrate the ability of AI to predict beyond its factual knowledge. Lateral AI is a unique and alternative option to other AI models, contributing to the diverse and creative pool of emerging AI technologies and applications. The principles behind Lateral AI can be used to simulate diverse communities in a variety of settings such as online virtual communities and human robotics.
DTP: An Open-Domain Text Relation Extraction Method

Open-domain text relation extraction (OpenTRE) is a subfield of information extraction that focuses on extracting relational facts from open-domain corpora. Recent OpenTRE researches have shown that clustering unlabeled instances leveraging knowledge from labeled data is effective, but most of them are based on the assumption that the testing set only contains open relations, which is inconsistent with real-world scenarios where known and open relations are mixed. Therefore, a novel OpenTRE method based on Dynamic Thresholds and Pair-based Self-weighting Loss (DTP) is proposed. It performs text data processing by categorizing instances and predicting unknown relations, which can handle a more diverse range of data. Specifically, we break down OpenTRE into two stages: detecting and discovering, which makes the OpenTRE process more understandable. Wherein, sample-based dynamic threshold strategy is employed to clarify the relation boundaries. Additionally, pair-based self-weighted loss improves the capture of semantic knowledge in labeled data and guides clustering. Experimental results indicate that this method outperforms strong baseline models on two datasets and has significant improvements.
Deblur Capsule Networks

Blur is often caused by physical limitations of the image acquisition sensor or by unsuitable environmental conditions. Blind image deblurring recovers the underlying sharp image from its blurry counterpart without further knowledge regarding the blur kernel or the sharp image itself. Traditional deconvolution filters are highly dependent on specific kernels or prior knowledge to guide the deblurring process. This work proposes an end-to-end deep learning approach to address blind image deconvolution in three stages: (i) it first predicts the blur type, (ii) then it deconvolves the blurry image by the identified and reconstructed blur kernel, and (iii) it deep regularizes the output image. Our proposed approach, called Deblur Capsule Networks, explores the capsule structure in the context of image deblurring. Such a versatile structure showed promising results for synthetic uniform camera motion and multi-domain blind deblur of general-purpose and remote sensing image datasets compared to some state-of-the-art techniques.
Knowledge Prompting with Contrastive Learning for Unsupervised CommonsenseQA

Unsupervised commonsense question answering is an emerging task in NLP domain. In this task knowledge is of vital importance. Most existing works focus on stacking large-scale models or extracting knowledge from external sources. However, these methods suffer from either the unstable quality of knowledge or the deficiency in the model's flexibility. In this paper, we propose a Knowledge Prompting with Contrastive Learning (KPCL) model to address these problems. Specifically, we first consider dropout noise as augmentation for commonsense questions. Then we apply unsupervised contrastive learning in further pre-training to capture the nuances among questions, and thus help the subsequent knowledge generation. After that, we utilize generic prompts to generate question-related knowledge descriptions in a zero-shot manner, facilitating easier transfer to new domains. Moreover, we concatenate knowledge descriptions with the commonsense question, forming integrated question statements. Finally, we reason over them to score the confidence and make predictions. Experimental results on three benchmark datasets demonstrate the effectiveness and robustness of our proposed KPCL, which outperforms baseline methods consistently.
Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices

Automatic Sign Language Recognition (SLR) is a critical step in facilitating communication between deaf and hearing people. An interesting application of such a technology is a real-time mobile sign language translator since it could integrate both groups more easily. To this end,troduce a neBrazilian sign language (LIBRAS) recognition approach, the first for a mobile environment using an efficient 3D Convolutional Neural Network (CNN) to classify a sequence of frames extracted from a word being signaled in a video. Results show that our model is aproximately24 to 81 times faster than recent works in the field, and it is tested on a mobile device to understand the trade-off between performance and accuracy. Although slightly low accuracy, we have a significantly faster model at inference time and the beginning of something more relevant in the field, creating a discussion of future points of improvement to obtain an efficient real-time sign language system without greatly sacrificing accuracy in LIBRAS classification.
Fast, Memory-Efficient Spectral Clustering with Cosine Similarity

Spectral clustering is a popular and effective method but known to face two significant challenges: scalability and out-of-sample extension. In this paper, we extend the work of Chen (ICPR 2018) on the speed scalability of spectral clustering in the setting of cosine similarity to deal with massive or online data that are too large to be fully loaded into computer memory. We start by assuming a small batch of data drawn from the full set and develop an efficient procedure that learns both the nonlinear embedding and clustering map from the sample and extends them easily to the rest of the data as they are gradually loaded. We then introduce an automatic approach to selecting the optimal value of the sample size. The combination of the two steps leads to a streamlined memory-efficient algorithm that only uses a small number of batches of data (as they become available), with memory and computational costs that are independent of the size of the data. Experiments are conducted on benchmark data sets to demonstrate the fast speed and excellent accuracy of the proposed algorithm. We conclude the paper by pointing out several future research directions.
Applications of Quantum Embedding in Computer Vision

Nowadays, Deep Neural Networks (DNNs) are fundamental to many vision tasks, including large-scale visual recognition. As the primary goal of the DNNs is to characterize complex boundaries of thousands of classes in a high-dimensional space, it is critical to learn higher-order representations for enhancing nonlinear modeling capability. Recently, a novel method called Quantum-State-based Mapping (QSM) has been proposed to improve the feature calibration ability of the existing attention modules in transfer learning tasks. QSM uses the wave function describing the state of microscopic particles to map the feature vector into the probability space. In essence, QSM introduces a novel higher-order representation to improve the nonlinear capability of the network. In this paper, we extend QSM to Quantum Embedding (QE) for designing new attention modules and Self-Organizing Maps, a class of unsupervised learning methods. We also conducted experiments to validate the effectiveness of QE.
Efficient Prompt Tuning for Vision and Language Models

Recently, large-scale pre-trained visual language models have demonstrated excellent performance in many downstream tasks. A more efficient adaptation method for different downstream tasks is prompt tuning, which fixes the parameters of the visual language model and adjusts only prompt parameters in the process of adapting the downstream tasks, using the knowledge learned by the visual language model during pre-training to solve the problems in the down-stream tasks. However, the loss of the downstream task and the original loss of the visual language model are not exactly same during model training. For example, CLIP uses contrast learning loss to train the model, while the downstream image classification task uses the cross-entropy loss commonly used in classification problems. Different loss has different guiding effects on the task. The trend of the accuracy of the visual language model task during training is also different from that with the downstream task. The choice of an appropriate loss function and a reasonable prompt tuning method have a great impact on the performance of the model. Therefore, we pro-pose a more efficient method of prompt tuning for CLIP, experiments on 11 datasets demonstrate that our method achieves better performance and faster convergence in the downstream task.
Adaptive Multi-hop Neighbor Selection for Few-Shot Knowledge Graph Completion

Few-shot Knowledge Graph Completion (FKGC) is a special task proposed for the relations with only a few triples. However, existing FKGC models face the following two issues: 1) these models cannot fully exploit the dynamic relation and entity properties of neighbors to generate discriminative representations; 2) these models cannot filter out noise in high-order neighbors to obtain reliable entity representations. In this paper, we propose an adaptive multi-hop neighbor selection model, namely AMBLE, to mitigate these two issues. Specifically, AMBLE first introduces a query-aware graph attention network (QAGAT) to obtain entity representations by dynamically aggregating one-hop neighbors based on relations and entities. Then, AMBLE aggregates high-order neighbors by iterating QAGAT and LSTM, which can efficiently extract useful and filter noisy information. Moreover, a Transformer encoder is used to learn the representations of subject and object entity pairs. Finally, we build an attentional matching network to map the query to few support triples. Experiments show that AMBLE outperforms state-of-the-art baselines on two public datasets.
Enhancement of Masked Expression Recognition Inference via Fusion Segmentation and Classifier

Despite remarkable advancements in facial expression recognition, recognizing facial expressions from occluded facial images in real-world environments remains a challenging task. Various types of occlusions randomly occur on the face, obstructing relevant information and introducing unwanted interference. Moreover, occlusions can alter facial structures and expression patterns, causing variations in key facial landmarks. To address these challenges, we propose a novel approach called the Occlusion Removal and Information Capture (ORIC) network, which fuses segmentation and classification. Our method consists of three modules: the Occlusion Awareness (OA) module learns about occlusions, the Occlusion Purification (OP) module generates robust and multi-scale occlusion masks to purify the occluded facial expression information, and the Expression Information Capture (EIC) module extracts comprehensive and robust expression features using the purified facial information. ORIC can eliminate the interference caused by occlusion and utilize both local region information and global semantic information to achieve facial expression recognition under occluded conditions. Through experimental evaluations on synthetic occluded RAF-DB and AffectNet datasets, as well as a real occluded dataset FED-RO, our method demonstrates significant advantages and effectiveness. Our research provides an innovative solution for recognizing facial expressions from occluded facial images in wild environments.
Topic Modeling for Short Texts via Adaptive P$$\acute{o}$$lya Urn Dirichlet Multinomial Mixture

Inferring coherent and diverse latent topics from short texts is crucial in topic modeling. Existing approaches leverage the Generalized P $$\acute{o}$$ lya Urn (GPU) model to incorporate external knowledge and improve topic modeling performance. While the GPU scheme successfully promotes similarity among words within the same topic, it has two major limitations. Firstly, it assumes that similar words contribute equally to the same topic, disregarding the distinctiveness of different words. Secondly, it assumes that a specific word should have the same promotion across all topics, overlooking the variations in word importance across different topics. To address these limitations, we propose a novel Adaptive P $$\acute{o}$$ lya Urn (APU) scheme, which builds topic-word correlation according to the external and local knowledge, and the Adaptive P $$\acute{o}$$ lya Urn Dirichlet Multinomial Mixture (APU-DMM) model that uses the topic-word correlation as an adaptive weight to promote topic inference process. Our extensive experimental study on three benchmark datasets shows the superiority of our model in terms of topic coherence and topic diversity over the eight baseline methods (The code is available at https://github.com/ddwangr/APUDMM ).
Synchronizable Fair Exchange

Fitzi, Garay, Maurer, and Ostrovsky (J. Cryptology 2005) showed that in the presence of a dishonest majority, no primitive of cardinality $$n - 1$$ is complete for realizing an arbitrary n-party functionality with guaranteed output delivery. In this work, we introduce a new 2-party primitive $$\mathcal {F}_{\textsf{SyX}}$$ (“synchronizable fair exchange”) and show that it is complete for realizing any n-party functionality with fairness in a setting where all parties are pairwise connected by instances of $$\mathcal {F}_{\textsf{SyX}}$$ . In the $$\mathcal {F}_{\textsf{SyX}}$$ -hybrid model, the two parties load $$\mathcal {F}_{\textsf{SyX}}$$ with some input, and following this, either party can trigger $$\mathcal {F}_{\textsf{SyX}}$$ with a “witness” at a later time to receive the output from $$\mathcal {F}_{\textsf{SyX}}$$ . Crucially the other party also receives output from $$\mathcal {F}_{\textsf{SyX}}$$ when $$\mathcal {F}_{\textsf{SyX}}$$ is triggered. The trigger witnesses allow us to synchronize the trigger phases of multiple instances of $$\mathcal {F}_{\textsf{SyX}}$$ , thereby aiding in the design of fair multiparty protocols. Additionally, a pair of parties may reuse a single a priori loaded instance of $$\mathcal {F}_{\textsf{SyX}}$$ in any number of multiparty protocols (involving different sets of parties). (The authors grant IACR a non-exclusive and irrevocable license to distribute the article under the https://creativecommons.org/licenses/by-nc/3.0/ ), (This work was done in part while all the authors were at MIT).