Reviewerly/exHarmony huggingface dataset:
Double Constacyclic Codes Over Two Finite Commutative Chain Rings

Many kinds of codes which possess two cycle structures over two special finite commutative chain rings, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {Z}}_{2}{\mathbb {Z}}_{4}$ </tex-math></inline-formula> -additive cyclic codes and quasi-cyclic codes of fractional index etc., were proved asymptotically good. In this paper we extend the study in two directions: we consider any two finite commutative chain rings with a surjective homomorphism from one to the other, and consider double constacyclic structures. We construct an extensive kind of double constacyclic codes over two finite commutative chain rings. And, developing a probabilistic method suitable for quasi-cyclic codes over fields, we prove that the double constacyclic codes over two finite commutative chain rings are asymptotically good.
Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNs

Emerging event-based social networks (EBSNs), such as Meetup, have grown rapidly and become popular in recent years.EBSNs differ from conventional social networks such as Facebook in that they not only involve online social interactions but also include offline, in-person interactions.Thus, EBSNs are naturally heterogeneous and possess more valuable social information.Group recommendations in EBSNs are typically only based on the interest information filled in by users, or friends' group information.Both these methods may not well reflect users' real intentions.In this study, we propose a recommender system to predict groups that may interest EBSN users, based on a novel heterogeneous augmented graph method and a random walk with restart algorithm.In this approach, online and offline social interactions are combined into a single heterogeneous augmented graph capturing all useful relationships, including userto-group relationships, user-to-event relationships, user-to-attribute relationships, and group-to-attribute relationships, and among others.To our knowledge, this work is the first attempt to apply a random walk algorithm into group recommendation in EBSNs.Extensive experiments on Meetup datasets demonstrate that our proposed recommender system achieves better results in terms of recall, precision, F-Measure and MRR metrics in comparison with the other four commonly used algorithms, including random recommendation, interest-based recommendation, interest-and neighborhood-based recommendation, and Katz Centrality.The significant recommendation performance of our approach may further enhance user satisfaction of EBSNs.Moreover, our approach to group recommendation may also be extended to other recommendationrelated applications such as event or friend recommendation.
Deep Skill Chaining with Diversity for Multi-agent Systems*

Multi-agent reinforcement learning requires the reward signals given by the environment to guide the convergence of individual agents' policy networks. However, in a high-dimensional continuous space, the non-stationary environment may provide outdated experiences that lead to the inability to converge. The existing methods can be ineffective in achieving a satisfactory training performance due to the inherent non-stationary property of the multi-agent system. We propose a novel reinforcement learning scheme, MADSC, to generate an optimized cooperative policy. Our scheme utilizes mutual information to evaluate the intrinsic reward function that can generate a cooperative policy based on the option framework. In addition, by linking the learned skills to form a skill chain, the convergence speed of agent learning can be significantly accelerated. Hence, multi-agent systems can benefit from MADSC to achieve strategic advantages by significantly reducing the learning steps. Experiments are performed on the SMAC multi-agent tasks with varying difficulties. Experimental results demonstrate that our proposed scheme can effectively outperform the state-of-the-art methods, including IQL, QMIX, and hDQN, with a single layer of temporal abstraction.
Data-Driven Robust Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) in the collaborative setting aims to find a joint policy that maximizes the accumulated reward averaged over all the agents. In this paper, we focus on MARL under model uncertainty, where the transition kernel is assumed to be in an uncertainty set, and the goal is to optimize the worst-case performance over the uncertainty set. We investigate the model-free setting, where the uncertain set centers around an unknown Markov decision process from which a single sample trajectory can be obtained sequentially. We develop a robust multi-agent Q-learning algorithm, which is model-free and fully decentralized. We theoretically prove that the proposed algorithm converges to the minimax robust policy, and further characterize its sample complexity. Our algorithm, comparing to the vanilla multi-agent Q-learning, offers provable robustness under model uncertainty without incurring additional computational and memory cost.
Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning

Federated learning (FL) can protect data privacy but has difficulties in motivating user equipment (UE) to engage in task training. This letter proposes a Bertrand-game based framework to address the incentive problem, where a model owner (MO) issues an FL task and the employed UEs help train the model by using their local data. Specially, we consider the impact of time-varying task load and channel quality on UE’s motivation to engage in the FL task. We adopt the finite-state discrete-time Markov chain (FSDT-MC) to predict these parameters during the FL task. Depending on the performance metrics set by the MO and the estimated energy cost of the FL task, each UE seeks to maximize its profit. We obtain the Nash equilibrium (NE) of the game in closed form, and develop a distributed iterative algorithm to find it. Finally, the simulation result verifies the effectiveness of the proposed approach.
The development of lemma and meaning in the language variety used by adolescents on social media

This study examines the natural phenomena on the development of lemma and meaning in the variety of languages used by adolescents on social media. Research design was done naturally through the process of observation, taking notes, and writing down the case. The qualitative research appears in the development of the entry which is easily found as a definite phenomenon of linguistic development. The data collection method was carried out by tracing data on the use of various languages on social media. To analyze the data, the researcher uses padan and agih methods. This method is to test the accuracy of classification on the types of language used by adolescents in social media in the development of the entry and or the development of the meaning of the entry. The results showed that the variety of languages on social media is very dynamic, developing, arbitrary, but conventional. The variety of languages indicates the development of new lemmas, acronyms, and walikan. The development of meaning is marked along with the development of the entry in the form of synonyms and the use of the Indonesian language entry in the form of a polysemic. Based on the results of this study, adolescents can be considered a productive period in exploring the language, through direct interaction or social media. Becoming actively productive in language exploration led to a potential for concepting a new lemma and a new meaning according to the context of language use. In addition, this development becomes one proof of the self-existence of adolescence, community characteristics, and also the need for the development of Indonesian vocabulary.
3D Face Reconstruction with Mobile Phone Cameras for Rare Disease Diagnosis

Computer vision technology is advancing rare disease diagnosis to address unmet needs of the more than 300 million individuals affected globally; one in three rare diseases have a known facial phenotype. 3D face model reconstruction is a key driver of these advances. However, the utility of 3D reconstruction from images obtained from mobile phone cameras has been questionable due to relatively low quality 2D data and need external calibration methods (e.g. visual markers) to extract accurate measurements. Herein a novel implementation pipeline, leveraging deep learning technologies, that can successfully reconstruct 3D face models from multiple 2D images taken by mobile phone cameras for clinician usage is described. Specifically, Multi-view Stereo (MVS) has been introduced to this application for providing a cost-effective pipeline of 3D face dense reconstruction. As a state-of-the-art MVS method, deep-learning based MVS has shown its strong generalization capability of using the low quality 2D face images to reconstruct 3D face models without camera calibration. The results demonstrate conceptual proof of a analytic pipeline to satisfy the clinician's needs.
Improving Nuclei Segmentation in Pathological Image via Reinforcement Learning

Nuclei segmentation of pathological images is an important task in computational pathology. Accurate segmentation results can help pathologists diagnose cancer more accurately. However, pathological images often exist on issues of cell aggregation, stacking, and uneven staining. Generally, we use the deep learning method to get relatively accurate segmentation results and then use the hard threshold with a probability of 0.5 as a means of foreground and background separation. While the hard threshold often leads to cell boundary loss and cell misclassification. To overcome these issues, we present an adaptive threshold segmentation approach based on reinforcement learning (RL), which takes the model output as an environment variable, and adaptively gives a threshold of foreground and background segmentation. Our proposed method uses Q- learning and Deep Q Network (DQN) algorithm in RL and analyzes their effects. The experimental results show that Q- learning and DQN have obtained 3.6% and 3.8% average IoU improvement respectively on the Multi-organ Nuclei Segmentation (MoNuSeg) dataset, compared to the hard threshold segmentation results. Furthermore, compared with other representative methods, our DQN based method obtains the state-of-the-art nuclei segmentation performance.
Fine-tuning Faster-RCNN tailored to Feature Reweighting for Few-shot Object Detection

Few-shot object detection has drawn more attention in computer vision now. One acknowledged task setting is that train the network to detect both of the base classes with abundant images and novel classes with only a few. Under this scenario, two classic pipelines of few-shot object detection are developed. One is fine-tuning, which trains the detection network on images of base classes and fine-tunes the last layer on images of both base and novel classes. The other one is meta learning, in which one pioneering model utilizes a meta-learner to transform supporting images into reweighting vectors, which are used to reweight features of the query images obtained through the feature extractor. A typical meta learning method splits the training process into two phases: meta-training and meta-testing. Firstly in the meta-training phase, the model is trained on base classes, then on both base and novel classes. In this paper, we synthesize these two pipelines together. For the network structure, we tailor Faster-RCNN to the reweighting module; for training, we follow the meta-training procedure and fine-tune the reweighting module and only the last layer of Faster-RCNN during meta-testing. Experiments on NWPU VHR-10 images show that our method improves the mAP by about 10 ∼ 20 percentages than both of the reweighting and fine-tuning methods.
Event Ontology about Wheat Cultivation

The article presents the development of the WheatEventOntology that includes events in the cultivation of winter wheat. The ontology divides events into two main types – domain events and emergency events. Each of the domain events leads to a state, in which the wheat is present during cultivation. Emergency events are conditions of wheat or soil, the overlook of which will lead to reduced yields. The WheatEventOntology is created to be integrated into an intelligent agricultural environment called ZEMELA. It is implemented from intelligent components that use the ontology to warn of various wheat-growing events, which facilitate the work of farmers.
Eliminating Micro-Architectural Side-Channel Attacks using Near Memory Processing

Secure architectures are becoming an increasingly common demand. This is due in large part to the rise of cloud computing, as users are trusting their data with hardware that they do not own. Unfortunately, many secure computation and isolation techniques are still susceptible to side-channel attacks. While various defenses to side-channel attacks exist, each tends to be targeted to a specific vulnerability and comes with a high runtime overhead, making it difficult to combine these defenses together in a performant manner.This work proposes an efficient design for preventing a large range of cache side-channel attacks by leveraging a near-memory processing (NMP) architecture. Specifically, the proposed design stores all sensitive data in isolated NMP vaults and performs all computation involving that sensitive data on NMP cores. Our approach eliminates possible cache side-channels while also minimizing runtime overhead when the parallelizability of NMP architecture is leveraged. Simulation results from a cycle-accurate architecture model shows that offloading secure computation to NMP cores can have as little as 0.26% overhead.
Cross-Lingual Named Entity Recognition for Heterogenous Languages

Previous works on cross-lingual Named Entity Recognition (NER) have achieved great success. However, few of them consider the effect of language families between the source and target languages. In this study, we find that the cross-lingual NER performance of a target language would decrease when its source language is changed from the same (homogenous) into a different (heterogenous) language family with that target language. To improve the NER performance in this situation, we propose a novel cross-lingual NER framework based on self-distillation mechanism and Bilateral-Branch Network (SD-BBN). SD-BBN learns source-language NER knowledge from supervised datasets and obtains target-language knowledge from weakly supervised datasets. These two kinds of knowledge are then fused based on self-distillation mechanism for better identifying entities in the target language. We evaluate SD-BBN on 9 language datasets from 4 different language families. Results show that SD-BBN tends to outperform baseline methods. Remarkably, when the target and source languages are heterogenous, SD-BBN can achieve a greater boost. Our results might suggest that obtaining language-specific knowledge from the target language is essential for improving cross-lingual NER when the source and target languages are heterogenous. This finding could provide a novel insight into further research.
Making Internet of Things Robust Through Blockchain Technology: A Quantitative Approachin the World of Technology Advancements

The domain of connectivity and linkages and new expertise is developed named the Internet of things or IoT.The technology paradigm of the manufacturing age was one of the machinesriseseparateautomated systems, there remainedcorporeal in nature mono purposeful and computerized. It is the biosphere of non-responsive inaccessible technologies, each of which the end-user must manage separately. The technology model of the evidence age is one of the amenities. It is a biosphere where separateconstituentskills are instrumented and associated into large network devices that can interconnect peer to peer to familiarize and self-organize the end-users needs to deliver a seamless service. One of the best artworks of this is the smart city where different organizations no longer exist in storage towers but are inter-connected and prearranged around the end-users need. A sample of 177 respondents was collected from respondents through, a "standard questionnaire," which was created on a five-point interval scale.
Mobile Live Coding Development as a Disruptive Learning Media in Programming Course

Learning media is a tool that can help and support teaching and learning activities so that they can run well and smoothly. A good learning media can the meaning of the message more clearly including the flow of learning objectives, materials, evaluations, and even communication media, and practicum media. The mobile application in this study contains the flow of learning objectives as a reference for achieving learning objectives, text and videos, discussion forums as communication media, live coding media for practicum, and quizzes as evaluation material. The research method used is Research and Development (RnD) or development research. The development process uses the ADDIE method (Analysis, Design, Development, Implementation, and Evaluation). The problem in the introduction states that students have limitations in accessing learning resources, so using this application can increase student interest in learning. Using the live coding feature in this application can help students understand C++ programming and measure their understanding of the material contained in the mobile application. From the research conducted, that the average percentage of respondents after using the Sinau Coding application is 83,3% and it can be concluded that this application is valid and feasible criterion for use in the field of education.
An Unsupervised Threshold-based GrowCut Algorithm for Mammography Lesion Detection

Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location of the abnormality. We present a method to automatically define the foreground seeds used in GrowCut. For experiments, mammograms from mini-MIAS dataset are used and a precision of 93.63% for the foreground seeds masks is achieved, which leads to promising segmentation results.
CMJRT: Cross-Modal Joint Representation Transformer for Multimodal Sentiment Analysis

Multimodal Sentiment Analysis (MSA) is an emerging research field that aims to identify the sentiment of speakers through Audio (A), Video (V), and Text (T) modalities. The major challenge is to capture joint representations that can associate and integrate information from various modalities. Most of the existing methods are prone to acquiring joint representation through the concatenation of input features. However, these methods are short of exploiting interactions fully to ensure consistency and complementarity among modalities. To solve this problem, we design a novel multimodal sentiment analysis framework named Cross-Modal Joint Representation Transformer (CMJRT), which exploits hierarchical interactions among modalities by passing joint representations from bimodality to unimodality. Specifically, we adopt cyclic translation to obtain joint representations of bimodality, where one modality is translated to the other modality forward and backward by encoder-decoders. The translation process ensures consistency between modalities. In addition, to explore complementarity among modalities, the cross-modal transformer is used to reinforce each unimodality with common information from bimodality. Extensive experiments on CMU-MOSI and CMU-MOSEI datasets demonstrate that our proposed method outperforms existing approaches.
Information and educational environment as a form of improving the didactical training of a future teacher

The purpose of the study is to determine the potential of the information and educational environment (IEE) in improving the didactic training of a future teacher. The article discusses the structural elements of the IEE, functioning on the basis of the Mordovia State Pedagogical University named after M. E. Evseviev: the educational environment "Moodle", the personal account of the future teacher, electronic textbooks and teaching aids contained on the platforms of electronic library systems. Scientific novelty lies in the described possibilities of using IEE in order to improve the didactic training of a future teacher. The work describes a complex of didactically filled digital elements that are part of the IEE MSPU, as well as presents some of the developed educational tasks when organizing such a training format. The article describes the experience of the Department of Pedagogy of the Mordovia State Pedagogical University named after M.E. Evseviev in the study of IEE as a form of improving didactic training. As a result, the possibilities were identified and the effectiveness of the use of IEE as a form of improving the didactic training of the future teacher was shown and showed positive dynamics under the condition of constantly updating and improving interactive content.
Credit-of-Q-value for Multi-Agent Reinforcement Learning

Recently, multi-agent reinforcement learning algorithms based on value function factorization have emerged to address the collaborative settings in multi-agent systems. These methods usually fit a joint Q-value function Qtot, which is the ground truth of the whole system and used for centralized training. Qtot can be decomposed into a set of local performance functions Qis, which have no specific meaning and are used as a reference for agents to take actions in a distributed manner. Inspired by the Age of Information concept and multi-scale continuous learning process in Biological Reinforcement Learning, this paper proposes an innovative concept Credit-of-Q-value (CoQ), which means how confident we can take actions based on the current Q-value function. Based on this concept, we propose a corresponding algorithm. We evaluate our method on a challenging set of StarCraft II tasks, and the results show that CoQ significantly improves state-of-the-art value-based multi-agent reinforcement learning methods.
Arachne: An Arkouda Package for Large-Scale Graph Analytics

Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes, new tools must be developed to enable data scientists to handle such graphs efficiently. These graphs may include social networks, computer networks, and genomes. In this paper, we propose a novel graph package Arachne to make large-scale graph analytics more effortless and more efficient based on the open-source Arkouda framework, which has been developed to allow users to perform massively parallel computations on distributed data with an interface similar to NumPy. In this package, we developed a fundamental sparse graph data structure and several useful graph algorithms around our data structure to build a basic algorithmic library. Benchmarks and tools have also been developed to evaluate and demonstrate the provided graph algorithms. The graph algorithms we have implemented thus far include breadth-first search (BFS), connected components (CC), k-Truss (KT), Jaccard coefficients (JC), triangle counting (TC), and triangle centrality (TCE). Their corresponding experimental results based on realworld and synthetic graphs are presented. Arachne is organized as an Arkouda extension package and is publicly available on GitHub (https://github.com/Bears-R-Us/arkouda-njit).