Reviewerly/exHarmony huggingface dataset:
GMValuator: Similarity-based Data Valuation for Generative Models

Data valuation plays a crucial role in machine learning. Existing data valuation methods have primarily focused on discriminative models, neglecting generative models that have recently gained considerable attention. A very few existing attempts of data valuation method designed for deep generative models either concentrates on specific models or lacks robustness in their outcomes. Moreover, efficiency still reveals vulnerable shortcomings. To bridge the gaps, we formulate the data valuation problem in generative models from a similarity-matching perspective. Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to provide data valuation for generation tasks. It empowers efficient data valuation through our innovatively similarity matching module, calibrates biased contribution by incorporating image quality assessment, and attributes credits to all training samples based on their contributions to the generated samples. Additionally, we introduce four evaluation criteria for assessing data valuation methods in generative models, aligning with principles of plausibility and truthfulness. GMValuator is extensively evaluated on various datasets and generative architectures to demonstrate its effectiveness.
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation

Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propose a simple yet efficient multi-modal fusion mechanism Linear Fusion, that performs better than the state-of-the-art multi-modal models even with limited supervision. Second, we propose M3L: Multi-modal Teacher for Masked Modality Learning, a semi-supervised framework that not only improves the multi-modal performance but also makes the model robust to the realistic missing modality scenario using unlabeled data. We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities. Our proposal shows an absolute improvement of up to 10% on robust mIoU above the most competitive baselines. Our code is available at https://github.com/harshm121/M3L
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate unbiased, privacy-preserving data while maintaining fidelity to the original data. However, assessing the trustworthiness of synthetic datasets and models is a critical challenge. We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases like education, healthcare, banking, and human resources, spanning different data modalities such as tabular, time-series, vision, and natural language. This holistic assessment is essential for compliance with regulatory safeguards. We introduce a trustworthiness index to rank synthetic datasets based on their safeguards trade-offs. Furthermore, we present a trustworthiness-driven model selection and cross-validation process during training, exemplified with "TrustFormers" across various data types. This approach allows for controllable trustworthiness trade-offs in synthetic data creation. Our auditing framework fosters collaboration among stakeholders, including data scientists, governance experts, internal reviewers, external certifiers, and regulators. This transparent reporting should become a standard practice to prevent bias, discrimination, and privacy violations, ensuring compliance with policies and providing accountability, safety, and performance guarantees.
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback

Many approaches to Natural Language Processing (NLP) tasks often treat them as single-step problems, where an agent receives an instruction, executes it, and is evaluated based on the final outcome. However, human language is inherently interactive, as evidenced by the back-and-forth nature of human conversations. In light of this, we posit that human-AI collaboration should also be interactive, with humans monitoring the work of AI agents and providing feedback that the agent can understand and utilize. Further, the AI agent should be able to detect when it needs additional information and proactively ask for help. Enabling this scenario would lead to more natural, efficient, and engaging human-AI collaborations. In this work, we explore these directions using the challenging task defined by the IGLU competition, an interactive grounded language understanding task in a MineCraft-like world. We explore multiple types of help players can give to the AI to guide it and analyze the impact of this help in AI behavior, resulting in performance improvements.
Classical-to-Quantum Sequence Encoding in Genomics

DNA sequencing allows for the determination of the genetic code of an organism, and therefore is an indispensable tool that has applications in Medicine, Life Sciences, Evolutionary Biology, Food Sciences and Technology, and Agriculture. In this paper, we present several novel methods of performing classical-to-quantum data encoding inspired by various mathematical fields, and we demonstrate these ideas within Bioinformatics. In particular, we introduce algorithms that draw inspiration from diverse fields such as Electrical and Electronic Engineering, Information Theory, Differential Geometry, and Neural Network architectures. We provide a complete overview of the existing data encoding schemes and show how to use them in Genomics. The algorithms provided utilise lossless compression, wavelet-based encoding, and information entropy. Moreover, we propose a contemporary method for testing encoded DNA sequences using Quantum Boltzmann Machines. To evaluate the effectiveness of our algorithms, we discuss a potential dataset that serves as a sandbox environment for testing against real-world scenarios. Our research contributes to developing classical-to-quantum data encoding methods in the science of Bioinformatics by introducing innovative algorithms that utilise diverse fields and advanced techniques. Our findings offer insights into the potential of Quantum Computing in Bioinformatics and have implications for future research in this area.
Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems

Minimax optimization plays an important role in many machine learning tasks such as generative adversarial networks (GANs) and adversarial training. Although recently a wide variety of optimization methods have been proposed to solve the minimax problems, most of them ignore the distributed setting where the data is distributed on multiple workers. Meanwhile, the existing decentralized minimax optimization methods rely on the strictly assumptions such as (strongly) concavity and variational inequality conditions. In the paper, thus, we propose an efficient decentralized momentum-based gradient descent ascent (DM-GDA) method for the distributed nonconvex-PL minimax optimization, which is nonconvex in primal variable and is nonconcave in dual variable and satisfies the Polyak-Lojasiewicz (PL) condition. In particular, our DM-GDA method simultaneously uses the momentum-based techniques to update variables and estimate the stochastic gradients. Moreover, we provide a solid convergence analysis for our DM-GDA method, and prove that it obtains a near-optimal gradient complexity of $O(\epsilon^{-3})$ for finding an $\epsilon$-stationary solution of the nonconvex-PL stochastic minimax problems, which reaches the lower bound of nonconvex stochastic optimization. To the best of our knowledge, we first study the decentralized algorithm for Nonconvex-PL stochastic minimax optimization over a network.
Revolutionizing Green Energy: AI's Game-Changing Role in Renewable Energy Solutions

This article explores the transformative potential of artificial intelligence (AI) in enhancing energy storage solutions, advancing forecasting models, discovering innovative materials and designs, and automating research and development processes for renewable energy sources. Real-world case studies are used to demonstrate the impact of AI in these key areas.
TraceNet: Tracing and Locating the Key Elements In&amp;nbsp;Sentiment Analysis

We study sentiment analysis task where the outcomes are mainly contributed by a few key elements of the inputs. Motivated by the two-streams hypothesis, we explore processing input items and their weights separately by developing a neural architecture, named TraceNet, to address this type of task. It not only learns discriminative representations for the target task via its encoders, but also traces key elements at the same time via its locators. In TraceNet, both encoders and locators are organized in a layer-wise manner, and a smoothness regularization is employed between adjacent encoderlocator combinations. Moreover, a sparsity constraint is enforced on locators for tracing purposes and items are proactively masked according to the item weights output by locators. A major advantage of TraceNet is that the outcomes are easier to understand, since the most responsible parts of inputs are identified. Also, under the guidance of locators, it is more robust to attacks due to its focus on key elements and the proactive masking training strategy. Experiments show its effectiveness for sentiment classification. Moreover, we provide case studies to demonstrate its robustness and interpretability.
An Examination of Copyright Issues Associated with Autonomous AI Machines

The advancements in technology such as Artificial intelligence (AI) systems in today’s technologically savvy world has led to the development of AI enabled systems capable of performing complex calculations, producing poetry, art work, and other more complex creative works. These advancements bring forth germane questions concerning Intellectual Property Rights, (IPR) for, they challenge not only traditional notions of concepts such as patent and copyright, but also lead to the emergence of questions related to the regulation of such creations amidst other legal issues. Utilising the doctrinal research method, this paper examines the extant legal framework on copyright law and juxtapose it with the current advancements in artificial intelligence system. The paper argues that based on the current position of the copyright law, AI are not legal persons and cannot own the intellectual property right in works generated by them. The paper recommends that the laws should be amended to reflect the current trends in the development and use of artificial intelligence.
Research on Encryption Protection Method of Quantum Security Computer

With the continuous development of Internet of Things (IoT) technology, information and communication technology is also progressing rapidly, among which quantum computer secrecy communication scheme is a new type of cryptographic lock system. It uses both traditional security software encryption algorithms and classical cryptographic systems to achieve a series of operations such as secret storage, transmission and restoration of data. This paper introduces the principle of quantum key distribution and its applications from the physical level; then analyses its security problems and the corresponding research status and proposes improvement methods and measures; finally, with the goal of "bit-based computing information security", a new type of secure communication scheme is designed.
Design of a digital service platform for college student employment based on big data analysis

In recent years, the employment situation of college students has become increasingly severe. In order to effectively solve the problems such as information asymmetry between the needs of college students and employers, lagging career guidance planning, outdated professional learning content in colleges and universities, this paper focuses on big data analysis technology to build a digital information service platform for college graduates' employment oriented by the needs of the talent market. Through the application of real cases, big data analysis technology can give a multi-dimensional and visual description of the recruitment needs of enterprises and predict the employment trend of college students in the analysis of massive recruitment data. It has obvious application value in solving the information asymmetry between graduates and enterprises, promoting the employment speed of college graduates, and improving the accuracy of job recommendation.
Improved CharNet text recognition algorithm based on multi-layer feature mechanism

Knowledge is the knowledge, judgment or skill acquired through learning, practice or exploration. The rapid development of informatization has driven the attention of enterprise knowledge management. Knowledge management is an activity of planning and managing knowledge, the process of knowledge creation and the application of knowledge. In the collection, transmission, storage, processing, sharing, communication, dynamic release and statistical analysis of knowledge information resources, technical means need to be adopted to make the knowledge in the text explicit. CharNet character detection and recognition algorithm in character recognition, the recognition accuracy of English characters is high, but the recognition of Chinese characters is difficult and can not adapt to complex scenes. This paper optimizes it through multi-layer feature mechanism, and experiments show that the optimized CharNet algorithm has a significant improvement in text recognition rate.
Do Learning and Texting Mix? Interview With Seungyeon Lee, PhD

We all know that texting and driving don’t mix. But what about texting in the classroom? Dr. Seungyeon Lee shares how your phone can influence your ability to process and retain new information.
Automated Detection of Voice in News Text – Evaluating Tools for Reported Speech and Speaker Recognition

The automated content analysis of text has become integral to contemporary communication and journalism research. However, automated approaches are seldom utilized to analyze reported voice in text, while doing so would offer valuable insights into media and communication practices. Bridging the fields of communication science and computational linguistics, this study reviews and evaluates off-the-shelf tools for automated voice detection (of direct/indirect speech and of speakers) with respect to user experience and validity. Manually annotated English news articles and Twitter data served as baseline for evaluating the automated detection of voice. Findings indicate that the tools being assessed offer a satisfactory user experience and provide promising solutions for detecting direct speech automatically, encouraging fellow researchers to utilize automated detection for direct quotations. However, the recognition of indirect speech and speakers needs considerable improvement.
A Survey on the Personnel Changes of Teachers in Special Needs Schools in Japan: Focusing on Interviews with University Teachers in Training Schools

This study aims to identify the issues related to the personnel transfer of teachers involved in special needs education in Japan.The participants were five university faculty members who trained teachers in special needs education.A survey was conducted from June to September 2022 using semi-structured interviews.The data obtained were analysed based on similarity, generating 10 categories: (1) personnel transfer system; (2) teacher employment system; (3) school administrators; (4) teachers' professional growth; (5) teacher evaluation; (6) collaboration among teachers; (7) professional qualifications other than teaching licenses; (8) school management of special needs schools; ( 9) challenges in the system for certifying teachers of special needs schools; (10) education-related laws.School authorities must consider strategies to solve the issues identified.This study suggests specific issues related to personnel transfers in special needs education.
PROFESSIONAL PRACTICE OF MASTER’S STUDENTS-UKRAINIAN PHILOLOGISTS ON THE BASIS OF PARTNERSHIP

The article outlines the peculiarities of the organization and conduct of master’s students professional practice according to the educational and professional program Secondary education (Ukrainian language and literature, English language) on the basis of partnership interaction. The requirements for the practical training of future teachers of the Ukrainian language and literature in the conditions of war are also summarized and include: training based on European criteria and standards and national practices, pedagogical partnership, introduction of info media literacy into the educational process. The specific principles of professional practice are clarified and contain student-centeredness, interdisciplinary integration, pedagogical partnership, infomedia literacy. The implementation of the principle of student-centeredness is realised in the “teacher-student” interaction, the teacher’s creation of conditions for the student’s self-development. The principle of partnership during the educational process is “manifested” in mutual respect, in the obligation to fulfil agreements. Professional practice is outlined as an interdisciplinary educational process, which is carried out based on partnership between institutions of higher pedagogical and general secondary education, allows mastering the key competencies of learning the Ukrainian language and literature, contributes to the formation of value orientations and an individual style of professional activity. The course of professional practice in Ukrainian language and literature in high school is presented on the basis of interregional partnership interaction of the resource centre for the professional development of teachers of Ukrainian language and literature of Sumy State Pedagogical University named after A.S. Makarenko, general secondary education institutions of the Sumy region, Ivan Zyazyun Institute of Pedagogical Education and Adult Education of the National Academy of Sciences of Ukraine and the Ukrainian Language and Information Fund of the National Academy of Sciences of Ukraine, National Academy of Sciences of Ukraine. Joint educational projects aimed at the restoration of Ukraine in the post-war period are also outlined, taking into account the nationbuilding priorities of teacher training, Ukrainian-language personality culture, media education, etc.
Approaches to Automatic Classification, Detection and Segmentation of Breast Arterial Calcification Using Deep Learning

Cardiovascular disease (CVD) is the leading cause of premature death in theUnited Kingdom, killing more than twice as many women as breast cancer.Conventional CVD risk factors have been shown to have less accuracy for femaleswho are considered low-risk. Recently, researchers have noted that breastarterial calcification (BAC), which is regularly observed as an incidentalfinding on mammograms, could be used to risk-stratify women forCVD.The grading of BAC, essential for thetriage and management of potential cardiovascular patients, is not astraightforward task, however. Manual BAC segmentation is known to be tedious,expensive, time-consuming and subjective. In order to automate the BAC gradingprocess, we investigated deep learning approaches to BAC classification, object detectionand segmentation using an anonymised dataset which was annotated for thepresence and location of BAC under the guidance of two consultant radiologists.Data augmentation was used in both the classification and object detection networks, increasing the training data size.Our modified ResNet22 network showedpromise in classifying the presence or absence of BAC at image level, attaininga test accuracy of 80%, indicating that this method could be used as a simpleflag for this purpose. We also used this network for feature extraction in aYOLOv4 (You Only Look Once) BAC object detection network. Despite improving on a recent similar study, this latter network performed poorly with very low average precision scores at several thresholds. More promising was ourDeepLabv3+-based BAC segmentation network which reached similarly high globalaccuracy scores to two other studies and a BF (Boundary F1) score of over 70% specifically for BAC.Future research involving larger datasets is needed in order to realise the potentialof concurrent, automatic BAC grading for women undergoing mammographic imaging.
Future Directions

Deep learning is an emerging technology with the promise of addressing the synergy between robotic perception and action. Research on learning-based robotics is still in the early stages and has not yet reached the same maturity level as traditional model-based algorithms. Yet, considerable progress has been made in the last few years, which has clearly shown the potential of deep learning to overcome some of the limitations of conventional model-based autonomy.
Applying and Evaluating Machine Learning Models for the Detection of Digital Image Steganography

Digital image steganography includes detecting hidden data. Machine learning models trained on standard and steganographic images can do this. Machine learning model research and adaptation for digital image steganography detection: Least Significant bit (LSB), discrete cosine Transformer (DCT), and F5 algorithms are steganography methods. Balanced datasets provide equal numbers of conventional and steganographic photos. Feature extraction: Extracting useful information from images for machine learning model training. Features include color histograms, texture characteristics, and wavelet transform. Support Vector Machine (SVM), random forest (RF), Ada boost Classifier (ada), and Bagging can detect steganography. Steganography technology and data complexity determine the model.Train and test the model: The specified machine learning model is trained on the preprocessed dataset using extracted features. The trained model is then evaluated for steganography detection on a separate dataset. Assess the model: Accuracy, precision, recall, and F1 score are used to evaluate the machine learning model. Changing feature extraction or machine learning algorithms can improve the model's performance. Deploy the model: After training and testing, the machine learning model can detect steganography in new photos. The model can be integrated into applications or used alone. In conclusion, studying and developing machine learning models to detect digital image steganography comprises finding strategies, collecting and preprocessing data, extracting features, selecting a model, training and testing it, evaluating its performance, and deploying it. It takes time and skill in steganography and machine learning.