Reviewerly/exHarmony huggingface dataset:
EM-Driven Unsupervised Learning for Efficient Motion Segmentation

In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation. However, in contrast to the classical iterative EM, once the network is trained, we can provide a segmentation for any unseen optical flow field in a single inference step and without estimating any motion models. We investigate different loss functions including robust ones and propose a novel efficient data augmentation technique on the optical flow field, applicable to any network taking optical flow as input. In addition, our method is able by design to segment multiple motions. Our motion segmentation network was tested on four benchmarks, DAVIS2016, SegTrackV2, FBMS59, and MoCA, and performed very well, while being fast at test time.
Machine‐learning‐based pilot symbol assisted channel prediction

In this paper, machine learning (ML) algorithms are used for channel prediction in wireless communications. The performances of five ML algorithms are compared in terms of the prediction accuracy and the symbol error rate (SER) of different modulation schemes based on the prediction. The result shows that, for channel prediction, support vector machine (SVM) has the best performance in terms of accuracy and stability. For signal detection, SVM and linear regression (LR) have their own advantages in different ranges of signal to noise ratio (SNR). At high constellation size, ML methods give similar performances to existing scheme. From the numerical examples, the SERs based on SVM and LR can both reach lower than 10−3 in binary phase shift keying and 16-ary quadrature amplitude modulation signalling, and can reach 1.13 × 10 − 2 $\times 10^{-2}$ and 4.28 × 10 − 3 $\times 10^{-3}$ in 16-ary phase shift keying signalling respectively. In terms of prediction time, SVM is more efficient.
Perfect Photon Indistinguishability from a Set of Dissipative Quantum Emitters

Single photon sources (SPS) based on semiconductor quantum dot (QD) platforms are restricted to low temperature (T) operation due to the presence of strong dephasing processes. Although the integration of QD in optical cavities provides an enhancement of its emission properties, the technical requirements for maintaining high indistinguishability (I) at high T are still beyond the state of the art. Recently, new theoretical approaches have shown promising results by implementing two-dipole-coupled-emitter systems. Here, we propose a platform based on an optimized five-dipole-coupled-emitter system coupled to a cavity which enables perfect I at high T. Within our scheme the realization of perfect I single photon emission with dissipative QDs is possible using well established photonic platforms. For the optimization procedure we have developed a novel machine-learning approach which provides a significant computational-time reduction for high demanding optimization algorithms. Our strategy opens up interesting possibilities for the optimization of different photonic structures for quantum information applications, such as the reduction of quantum decoherence in clusters of coupled two-level quantum systems.
A face detection ensemble to monitor the adoption of face masks inside the public transportation during the COVID-19 pandemic

The designing of ensembles is widely adopted when single machine learning methods fail to obtain satisfactory performances by analyzing complex data characterized by being imbalanced, high-dimensional, and noisy. Such a failure is a well-known statistical challenge when the learning algorithm searches for a model in a large space of hypotheses and the data do not significantly represent the problem, thus not inducing it from a space of admissible functions towards the best global model. We have addressed this issue in a real-world application, whose main objective was to identify whether users were wearing masks inside public transportation during the COVID-19 pandemic. Several studies have already pointed that face masks are an important and efficient non-pharmacological strategy to reduce the virus spread. In this sense, we designed an approach using Convolutional Neural Networks (CNN) to track the adoption of masks in different transportation lines, regions, days, and time. Aiming at reaching this goal, we propose an ensemble of face detectors and a CNN architecture, called MaskNet, to analyze all public-transport passengers and provide valuable information to policymakers, which are able to dedicate efforts to more effective advertisements and awareness work. In practice, our approach is running in a real scenario in Salvador (Brazil).
Acceptance of Serious Games to Develop Digital Competencies in Higher Education Professors

Serious games are the focus of the current research agenda. They show promise for allowing students to learn and practice skills. In fewer studies, they are used for the development of academic competencies. Therefore, it is of interest to deepen the research on the benefits of serious games in academic education and training. This article presents a study using serious games aimed at higher education academics’ training for the development of digital competencies. The study was carried out with 56 academics, using the serious game called AstroCódigo. The study analyzed the level of acceptance of serious games using the TAM model and what factors the academics believe affect this acceptance. It is important to know possible barriers that affect the development of actions oriented to the formation of academic competencies through serious games, which can also affect the adoption of games as a resource for teaching situations. None of prior studies have focused on analyzing the technological acceptance of serious games used for the development of academic competencies. The results of this research indicate that participants believe that using serious games can be beneficial to drive digital competencies. However, there are aspects such as the highest academic qualification, work experience, professional development, perceived usefulness, ease of use, and fear of change associated with age rank which can play a negative role in the use and acceptance of digital technologies by academics, particularly serious games. These results may be a clue to the barriers linked to the fact that most of the participants in this study do not use serious games in their classes or for their digital skills training. Additionally, the possibility to try and fail and the increased level of challenges proposed in the serious games, related to enjoyment, were valued by academics, during the sessions with AstroCódigo. These findings open the door to organize strategies for academic training in digital competencies within higher education institutions. They can also impact the design decisions of new serious games.
The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic Algorithm

Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and disease control which has a very dangerous impact as well as economic losses for farmers. The characteristics that are very visible on rice leaves have a greater area than other plant structures, rice leaves can be applied for early diagnosis of rice plant diseases. Fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means are the approaches employed (GA-FCM). The center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research is Teaching Sawah Farm IPB, and the secondary dataset is UCI Rice Leaf Diseases. According to the results of the comparison the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the silhoutte coefficient value compared to just 60% for FCM. This research shows the results that the proposed method can add 5% accuracy to the clustering results in terms of identifying the types of rice plant diseases properly.&#x0D;
Metamorphism of sulfide ores from the Aggeneys-Gamsberg base metal deposits, South Africa

Four major stratiform base metal deposits occur in the Aggeneys-Gamsberg area that are part of the poly-deformed and medium- to high-grade metamorphosed Namaqualand Metamorphic Complex of South Africa. The host rocks and the individual sulfide ore minerals have been intensely affected by several metamorphic and deformational events, resulting in almost total obliteration of primary textures and the formation of a range of structural, textural and mineralogical modifications typical of metamorphosed base metal deposits. Large-scale dislocation and reconcentration of the original disseminated mineralization as discordant ore pegmatites and massive sulfide segregates suggest extensive involvement of metamorphic fluids in the mobilization process during peak metamorphic conditions. Although the preserved fabric is predominantly of a retrograde character, the overall structural and textural relationships favor a syn-sedimentary origin of the mineralization over syntectonic emplacement during the main phase of deformation.
Quality Assessment of View Synthesis Based on Visual Saliency and Texture Naturalness

Depth-Image-Based-Rendering (DIBR) is one of the core techniques for generating new views in 3D video applications. However, the distortion characteristics of the DIBR synthetic view are different from the 2D image. It is necessary to study the unique distortion characteristics of DIBR views and design effective and efficient algorithms to evaluate the DIBR-synthesized image and guide DIBR algorithms. In this work, the visual saliency and texture natrualness features are extracted to evaluate the quality of the DIBR views. After extracting the feature, we adopt machine learning method for mapping the extracted feature to the quality score of the DIBR views. Experiments constructed on two synthetic view databases IETR and IRCCyN/IVC, and the results show that our proposed algorithm performs better than the compared synthetic view quality evaluation methods.
Entity-aware and Motion-aware Transformers for Language-driven Action Localization

Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language queries to video segments, but estimating precise boundaries is still under-explored. In this paper, we propose entity-aware and motion-aware Transformers that progressively localize actions in videos by first coarsely locating clips with entity queries and then finely predicting exact boundaries in a shrunken temporal region with motion queries. The entity-aware Transformer incorporates the textual entities into visual representation learning via cross-modal and cross-frame attentions to facilitate attending action-related video clips. The motion-aware Transformer captures fine-grained motion changes at multiple temporal scales via integrating long short-term memory into the self-attention module to further improve the precision of action boundary prediction. Extensive experiments on the Charades-STA and TACoS datasets demonstrate that our method achieves better performance than existing methods.
Clustering with Nature-Inspired Algorithm Based on Territorial Behavior of Predatory Animals

Clustering constitutes a well-known problem of division of unlabelled dataset into disjoint groups of data elements. It can be tackled with standard statistical methods but also with metaheuristics, which offer more flexibility and decent performance. The paper studies the application of the clustering algorithm—inspired by the territorial behaviors of predatory animals—named the Predatory Animals Algorithm (or, in short: PAA). Besides the description of the PAA, the results of its experimental evaluation, with regards to the classic k-means algorithm, are provided. It is concluded that the application of newly-created nature-inspired technique brings very promising outcomes. The discussion of obtained results is followed by areas of possible improvements and plans for further research.
Toward Policy Explanations for Multi-Agent Reinforcement Learning

Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions is crucial for improving system transparency, increasing user satisfaction, and facilitating human-agent collaboration. However, existing works on explainable reinforcement learning mostly focus on the single-agent setting and are not suitable for addressing challenges posed by multi-agent environments. We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) language explanations to answer queries about agent behavior. Experimental results on three MARL domains demonstrate the scalability of our methods. A user study shows that the generated explanations significantly improve user performance and increase subjective ratings on metrics such as user satisfaction.
METHODS OF LEARNING INDONESIAN LANGUAGE: ETHNOPRAGMA-EDUCATIVE PERSPECTIVE IN HIGH SCHOOL

The problems discussed in this study are based on the transition of curriculum changes that often change by government policies and the dominance of the use of English. This problem causes Indonesian proficiency value to be categorized as low. For this reason, a learning strategy in Indonesian is needed to minimize all impacts that occur by offering and modifying existing methods called ethnopragma-educative. This study aims to describe learning methods Indonesian in ethnopragma-educative-based high schools (communication ethnography, pragmatics, and educational interactions. The use of methods is carried out with qualitative description in order to be able to accommodate the reality of language in interactions in the classroom. Observation methods carry out the collection of methods, in-depth interviews, documentation with recording techniques, and pragmatics match techniques, and focusing discussion groups with teachers, stakeholders, and communities in the high school environment. The research results were obtained based on the modification of the three statements are (1) the use of communication ethnographic approaches include eight elements, namely setting and scene, participants, ends, an act of sequences, key, instrumentalities, a norm of interaction, and genres able to accommodate learning situations in the classroom to create effective and efficient learning; (2) the use of pragmatic approaches based on illocution, locution, and perlocution actions can maximize the role of teachers as motivators and inspirators in involving students to participate in Indonesian learning to develop cognitive, psychomotor, and achievement of learning go; (3) the use of educational interaction approaches can create two-way learning that is realized by the question and answer method between teachers and students in the classroom
The Impact of Artificial Intelligence Technology on Market Public Administration in a Complex Market Environment

Artificial intelligence (AI) technology is applied to various fields and industries with the rapid development of Internet technology. This study aims to promote the intelligent popularization of market public management strategies based on data analysis through the development and promotion of AI. AI technology is used to optimize the integration of the Market Public Administration organizational structure system, and machine algorithms process the data set. A public utility platform management system is established, and a complete public utility management assessment system and platform are based after classifying the data. Research has found that AI technology has been used in the Market Public Administration field to effectively improve work efficiency and solve the duplication and complexity of work in the management process. This has also opened a new chapter for the use of AI technology innovation. The results show that AI technology uses two driving logics of data and algorithms to intelligently solve the management problems and form a complete operating system to reflect the role of AI in the management system.
Develop English Electronic Module for Tourism Through Analysis of Learner’s and Context

Tourism development in Lampung province has been developing rapidly and the need for English communication skills at the tourism workplace is highly required due to a lot of visitors are foreigners. The state Polytechnic of Lampung has opened a new program study of tour and travel to support the government in the tourism sector and train students to have better English skills therefore need to develop good materials for teaching which meet with tourism industry. Developing good English materials for tourism based on an Android smartphone is needed since students lack academic hours for learning English. The objective of this research is to develop an electronic module (e-module) of English for tourism based on analysis learners and context that become important step in Dick and Carey Model. The development of e-module of English for tourism applied collaboration of research and development (R&amp;D) that focus on the third of ten steps of Dick and Carey Model. The result of this research is a prototype of e-module of English for tourism that has been validated and published on google play store.
WordRevert: Adversarial Examples Defence Method for Chinese Text Classification

Adversarial examples can evade the detection of text classification models based on Deep Neural Networks (DNNs), thus posing a potential security threat to the system.To address this problem, we propose an adversarial example defense method for Chinese text classification called WordRevert.The method first obtains the ''positive text'' containing the adversarial words by filtering the clauses that do not contribute to the current classification label.Then the detection network is combined with the position importance calculation function to achieve the detection of the adversarial words.Finally, the adversarial words are restored to the original words by calculating the candidate score and the detection score.The experiments show that the current popular Chinese text adversarial attack algorithms can be effectively defended by this method, and achieve a significant increase in the accuracy of the adversarial examples with a small reduction in the classification accuracy of clean samples while achieving better precision, recall, and F1 value of adversarial word detection and restoration.
Design a system for an approved video copyright over cloud based on biometric iris and random walk generator using watermark technique

Copyright is a tool for preventing anyone forged to copy an electronic work from another person and claim that electronic work is referred to him.Since the identity of the person is always determined by his name and biometrics, there is a concern to handle this information, to preserve the copyright.In this paper, a new idea for copyright technology is used to prove video copyright, by using blind watermarking technique, the ownership information is hidden inside video frames using linear congruential generator (LCG) for adapted the locations of vector features extracted from the name and biometric image of the owner instead of hidden the watermark in the Pseudo Noise sequences or any other feature extraction technique.When providing the watermarked vector, a statistical operation is used to increase randomization state for the amplifier factors of LCG function.LCG provides random positions where the owner's information is stored inside the video.The proposed method is not difficult to execute and can present an adaptable imperceptibility and robustness performance.The output results show the robustness of this approach based on the average PSNR of frames for the embedded in 50 frames is around 47.5 dB while the watermark remains undetectable.MSSIM values with range (0.83 to 0.99).
Robust fingerprinting of genomic databases

Abstract Motivation Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel’s law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. Results Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP–phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP–phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy. Availability and implementation https://github.com/xiutianxi/robust-genomic-fp-github.
Better Language Model with Hypernym Class Prediction

Class-based language models (LMs) have been long devised to address context sparsity in n-gram LMs.In this study, we revisit this approach in the context of neural LMs.We hypothesize that class-based prediction leads to an implicit context aggregation for similar words and thus can improve generalization for rare words.We map words that have a common WordNet hypernym to the same class and train large neural LMs by gradually annealing from predicting the class to token prediction during training.Empirically, this curriculum learning strategy consistently improves perplexity over various large, highly-performant state-of-the-art Transformer-based models on two datasets, WikiText-103 and ARXIV.Our analysis shows that the performance improvement is achieved without sacrificing performance on rare words.Finally, we document other attempts that failed to yield empirical gains, and discuss future directions for the adoption of class-based LMs on a larger scale.