Reviewerly/exHarmony huggingface dataset:
MultiLive: Adaptive Bitrate Control for Low-Delay Multi-Party Interactive Live Streaming

In multi-party interactive live streaming, each user can act as both the sender and the receiver of a live video stream. Designing adaptive bitrate (ABR) algorithm for such applications poses three challenges: (i) due to the interaction requirement among the users, the playback buffer has to be kept small to reduce the end-to-end delay; (ii) the algorithm needs to decide what is the bitrate to receive and what is the set of bitrates to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">send</i> ; (iii) the delay and quality requirements between each pair of users may differ, for instance, depending on whether the pair is interacting directly with each other. To address these challenges, we first develop a quality of experience (QoE) model for multi-party live streaming applications. Based on this model, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiLive</i> , an adaptive bitrate control algorithm for the multi-party scenario. MultiLive models the many-to-many ABR selection problem as a non-linear programming problem. Solving the non-linear programming equation yields the target bitrate for each pair of sender-receiver. To alleviate system errors during the modeling and measurement process, we update the target bitrate through the buffer feedback adjustment. To address the throughput limitation of the uplink, we cluster the ideal streams into a few groups, and aggregate these streams through scalable video coding for transmissions. We also deploy the algorithm on a commercial live streaming platform that provides such services for more than 2300 users. The experimental results show that MultiLive outperforms the fixed bitrate algorithm, with 2- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> improvement in average QoE. Furthermore, the end-to-end delay is reduced to around 100 ms, much lower than the 400 ms threshold recommended for video conferencing.
GSITK: A sentiment analysis framework for agile replication and development

GSITK is a framework to perform a wide variety of sentiment analysis tasks, including dataset acquisition, text preprocessing, model design, and performance evaluation. The framework is oriented to both researchers and practitioners, easing the replication of previous sentiment models, as well as offering implementations of common tasks. This is achieved by building several abstractions on top of popular libraries such as scikit-learn and NLTK. In this way, GSITK allows users to implement complex sentiment pipelines using comprehensible Python code. The framework is Open Source and has been used successfully in several research projects and competitions.
Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.
Hate or Non-hate: Translation based hate speech identification in Code-Mixed Hinglish data set

Hate speech identification in social media has emerged as a highly debated research topic in computational linguistics. Understanding linguistic phenomena in low-resource languages, in particular, remains a major problem in natural language processing. Code-mixing is a common phenomenon in social media writing, particularly in multilingual societies such as India. Traditional deep learning techniques trained on monolingual data will not perform well on code-mixed data, and training new models are challenging due to a lack of resources. Converting multilingual data into monolingual is an important solution to this challenge. TIF-DNN, a Transformer-based Interpretation and Feature Extraction Model is proposed in this work for hate speech identification. We used the IndicNLP and Englishtohindi libraries for transliteration and translation, respectively, and mBERT for feature extraction in our suggested model. Later, we compared our findings to various baseline and existing models.
Real time deterministic quantum teleportation over 10 km of single optical fiber channel

A real time deterministic quantum teleportation over a single fiber channel was implemented experimentally by exploiting the generated EPR entanglement at 1550 nm. A 1342 nm laser beam was used to transfer the classical information in real time and also acted as a synchronous beam to realize the synchronization of the quantum and classical information. The dependence of the fidelity on the transmission distance of the fiber channel was studied experimentally with optimizing the transmission efficiency of the lossy channel that was established to manipulate the beam of the EPR entanglement in Alice's site. The maximum transmission distance of the deterministic quantum teleportation was 10 km with the fidelity of 0.51 ± 0.01, which is higher than the classical teleportation limit of 1/2. The work provides a feasible scheme to establish metropolitan quantum networks over fiber channels based on deterministic quantum teleportation.
Toxicity detection in online Georgian discussions

Online social platforms have become omnipresent. While these environments are beneficial for sharing messages, ideas, or information of any kind, they also expose cyber-bullying, verbal harassment, or humiliation. Admittedly and regrettably, the latter actions are rampant, urging further research to restrain malicious activities. Even though this topic has been explored in several languages, there is no prior work in Georgian toxic comment analysis and detection. In this work, we extracted data from the Tbilisi forum, an online platform for public discussions. Data containing 10,000 comments were labeled as toxic/non-toxic. After data preprocessing, we pass generated vectors to our models. We developed multiple deep learning architectures: NCP, biRNN, CNN, biGRU-CNN, biLSTM, biGRU, transformer, and a baseline NB-SVM. We took a novel approach in toxic comment classification via employing a brain-inspired NCP model. Each model, including NCP, showed satisfactory results. Our best-performing model was CNN with 0.888 ACC and 0.942 AUC.
Real-space RG, error correction and Petz map

There are two parts to this work: First, we study the error correction properties of the real-space renormalization group (RG). The long-distance operators are the (approximately) correctable operators encoded in the physical algebra of short-distance operators. This is closely related to modeling the holographic map as a quantum error correction code. As opposed to holography, the real-space RG of a many-body quantum system does not have the complementary recovery property. We discuss the role of large $N$ and a large gap in the spectrum of operators in the emergence of complementary recovery. Second, we study the operator algebra exact quantum error correction for any von Neumann algebra. We show that similar to the finite dimensional case, for any error map in between von Neumann algebras the Petz dual of the error map is a recovery map if the inclusion of the correctable subalgebra of operators has finite index.
Iterative facial image inpainting based on an encoder-generator architecture

Facial image inpainting is a challenging problem as it requires generating new pixels that include semantic information for masked key components in a face, e.g., eyes and nose. Recently, remarkable methods have been proposed in this field. Most of these approaches use encoder–decoder architectures and have different limitations such as allowing unique results for a given image and a particular mask. Alternatively, some optimization-based approaches generate promising results using different masks with generator networks. However, these approaches are computationally more expensive. In this paper, we propose an efficient solution to the facial image painting problem using the Cyclic Reverse Generator (CRG) architecture, which provides an encoder-generator model. We use the encoder to embed a given image to the generator space and incrementally inpaint the masked regions until a plausible image is generated; we trained a discriminator model to assess the quality of the generated images during the iterations and determine the convergence. After the generation process, for the post-processing, we utilize a Unet model that we trained specifically for this task to remedy the artifacts close to the mask boundaries. We empirically observed that even in the absence of important facial features, the encoder model is capable of embedding images in semantically rich regions in the latent space, utilizing the surrounding context in the images. Cultivating the feedback loop between the encoder and generator gradually improves the missing content in the images in an iterative fashion, and only a few iterations are sufficient to generate realistic content. Since the models are not trained for particular mask types, our method allows applying sketch-based inpaintings, using a variety of mask types, and producing multiple and diverse results. We compared our method with the state-of-the-art models both quantitatively and qualitatively, and observed that our method can compete with the other models in all mask types; it is particularly better in images where larger masks are utilized. Our code, dataset and models are available at: https://github.com/yahyadogan72/iterative_facial_image_inpainting.
Adaptive stochastic path planning for robots – real-time optimization by means of neural networks

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Design of a Vaccine Passport Validation System Using Blockchain-based Architecture: Development Study

COVID-19 is an ongoing global pandemic caused by SARS-CoV-2. As of June 2021, 5 emergency vaccines were available for COVID-19 prevention, and with the improvement of vaccination rates and the resumption of activities in each country, verification of vaccination has become an important issue. Currently, in most areas, vaccination and reverse transcription polymerase chain reaction (RT-PCR) test results are certified and validated on paper. This leads to the problem of counterfeit documents. Therefore, a global vaccination record is needed.The main objective of this study is to design a vaccine passport (VP) validation system based on a general blockchain architecture for international use in a simulated environment. With decentralized characteristics, the system is expected to have the advantages of low cost, high interoperability, effectiveness, security, and verifiability through blockchain architecture.The blockchain decentralized mechanism was used to build an open and anticounterfeiting information platform for VPs. The contents of a vaccination card are recorded according to international Fast Healthcare Interoperability Resource (FHIR) standards, and blockchain smart contracts (SCs) are used for authorization and authentication to achieve hierarchical management of various international hospitals and people receiving injections. The blockchain stores an encrypted vaccination path managed by the user who manages the private key. The blockchain uses the proof-of-authority (PoA) public chain and can access all information through the specified chain. This will achieve the goal of keeping development costs low and streamlining vaccine transit management so that countries in different economies can use them.The openness of the blockchain helps to create transparency and data accuracy. This blockchain architecture contains a total of 3 entities. All approvals are published on Open Ledger. Smart certificates enable authorization and authentication, and encryption and decryption mechanisms guarantee data protection. This proof of concept demonstrates the design of blockchain architecture, which can achieve accurate global VP verification at an affordable price. In this study, an actual VP case was established and demonstrated. An open blockchain, an individually approved certification mechanism, and an international standard vaccination record were introduced.Blockchain architecture can be used to build a viable international VP authentication process with the advantages of low cost, high interoperability, effectiveness, security, and verifiability.
Learning Precoding Policy: CNN or GNN?

Optimizing precoding with deep learning enables its real-time implementation. In addition to the learning perfor-mance such as sum rate, training complexity is also important since neural networks (NNs) have to be re-trained in time-varying channels. By leveraging the prior-known property for a policy to be learned, inductive biases can be introduced to the structure of NNs to balance the learning performance and training com-plexity. Most existing works use convolutional neural networks for learning precoding policy, without considering whether their inductive biases match the precoding task. In this paper, we first show that full-digital precoding policy exhibits permutation equivariance property and introduce graph NN (GNN) to learn the policy. We then analyze and show the connections between the structures and inductive biases of several NNs. Simulation results show that the inductive bias of the GNN is well-matched to the precoding policy, and hence achieves higher sum-rate with given number of training samples and needs lower training complexity to achieve the same sum-rate than other NNs.
Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System

In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is applied for selecting the important features from the dataset. After applying a data transformation technique, the values normalized. Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance. The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). Classifier performance is evaluated by measures like accuracy and error rate. From the investigation, the proposed approach provided 86.2% of classification accuracy for the breast cancer dataset compared to other two approaches.
Did Tools Create Humans?

The conception and creation of tools, their design, refinements and uses are traditionally viewed as being direct elaborations of inherent human capabilities. Here, we offer an alternative to this traditional perspective. Using a tool to complete any given task serves to change that task which, in turn, impacts and alters the tools’ user via the performance of current and subsequent tasks. Moreover, as each task evolves, humans have come to shape additional tools to respond accordingly. These ever-increasing complexifications then serve to stimulate expansion in inherent human cognitive capabilities themselves. Here, we do not view humans as the initial creators of tools. Rather, the a priori presence of tools in the ambient environment explains, ab initio, why the species homo sapiens has evolved in the way that history records. We thus propose that tools create humans. The subsequent symbiosis between humans and those tools, portrayed as a cumulative spiral structure, serves to frame this evolution of elaborative technologies that have been used across time to achieve socially desired objectives. From our premise, we envision evident lines of progress that can be anticipated for the future of this human-tool dyad.
Cloud-Based Patient Health Information Exchange System Using Blockchain Technology

With the fast advancement of ubiquitous technologies and mobile computation, a massive quantity of individual health-related information is being created and gathered constantly at all occasions. These individual datasets include crucial data and are the property of the specific users, so they must be owned and governed by individuals. Presently, the majority of these datasets are held and managed by several wireless service suppliers, and this unified data storing poses data security vulnerabilities and impedes information sharing. Such access to patient information is significant tools for medical academic and clinical ventures. We present a theoretical architecture for revealing private perpetual clinical informational data utilizing blockchain solutions which are backed by cloud computing to exchange well-being data confidentially and responsibly in this academic work. In addition, to have control over data integrity, we develop an information quality control and assurance mechanism data mining and machine learning technologies. The proposed system’s major function is to empower users to securely possess, manage, and exchange their healthcare data in a protected manner by utilizing the Data Protection Regulation compliant manner to profit from their statistics. This also allows academics and conventional data users to acquire high-quality healthcare data for scientific and business reasons efficiently and effectively.
Memorial GAN With Joint Semantic Optimization for Unpaired Image Captioning

Most works of image captioning are implemented under the full supervision of paired image–caption data. Limited to expensive cost of data collection, the task of unpaired image captioning has attracted researchers’ attention. In this article, we propose a novel memorial GAN (MemGAN) with the joint semantic optimization for unpaired image captioning. The core idea is to explore implicit semantic correlation between disjointed images and sentences through building a multimodal semantic-aware space (SAS). Concretely, each modality is mapped into a unified multimodal SAS, where SAS includes the semantic vectors of image <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> , visual concepts <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O}$ </tex-math></inline-formula> , unpaired sentence <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> , and the generated caption <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> . We adopt the memory unit based on multihead attention and relational gate as a backbone to preserve and transit crucial multimodal semantics in the SAS for image caption generation and sentence reconstruction. Then, the memory unit is embedded into a GAN framework to exploit the semantic similarity and relevance in SAS, that is, imposing a joint semantic-aware optimization on SAS without supervision clues. To summarize, the proposed MemGAN learns the latent semantic relevance of SAS’s multimodalities in an adversarial manner. Extensive experiments and qualitative results demonstrate the effectiveness of MemGAN, achieving improvements over state of the arts on unpaired image captioning benchmarks.
Multi-Agent Incentive Communication via Decentralized Teammate Modeling

Effective communication can improve coordination in cooperative multi-agent reinforcement learning (MARL). One popular communication scheme is exchanging agents' local observations or latent embeddings and using them to augment individual local policy input. Such a communication paradigm can reduce uncertainty for local decision-making and induce implicit coordination. However, it enlarges agents' local policy spaces and increases learning complexity, leading to poor coordination in complex settings. To handle this limitation, this paper proposes a novel framework named Multi-Agent Incentive Communication (MAIC) that allows each agent to learn to generate incentive messages and bias other agents' value functions directly, resulting in effective explicit coordination. Our method firstly learns targeted teammate models, with which each agent can anticipate the teammate's action selection and generate tailored messages to specific agents. We further introduce a novel regularization to leverage interaction sparsity and improve communication efficiency. MAIC is agnostic to specific MARL algorithms and can be flexibly integrated with different value function factorization methods. Empirical results demonstrate that our method significantly outperforms baselines and achieves excellent performance on multiple cooperative MARL tasks.