Reviewerly/exHarmony huggingface dataset:
DDAM '22: 1st International Workshop on Deepfake Detection for Audio Multimedia

Over the last few years, the technology of speech synthesis and voice conversion has made significant improvement with the development of deep learning. The models can generate realistic and human-like speech. It is difficult for most people to distinguish the generated audio from the real. However, this technology also poses a great threat to the global political economy and social stability if some attackers and criminals misuse it with the intent to cause harm. In this workshop, we aim to bring together researchers from the fields of audio deepfake detection, audio deep synthesis, audio fake game and adversarial attacks to further discuss recent research and future directions for detecting deepfake and manipulated audios in multimedia.
Wingman: A New Movie Recommendation System Algorithm

In this rising new era, the world is growing faster and so are we. People nowadays need effortless services, and recommendation system is one of them. They are present and used everywhere, whether it's advertising, e-commerce or any other domain. Companies such as Netflix, YouTube, Amazon, Spotify rely on recommendation systems to boost their sales. Moreover, it increases the engagement of users with a particular app or website as it tends to show more relevant individualized content. It basically takes the user’s past history as an input and then using filtering techniques it predicts the most suitable item/content for the user as an output. The main objective of this paper is to predict movies most desired by the user with as much accuracy as possible. In this paper, I will provide a brief description of various filtering methods and the particular techniques that I used to achieve the desired result. I have used Tf-Idf to quantify the overview of the movies, then did a pairwise similarity for the Content Based Filtering. This allows the user to watch movies based on their watch history according to the plot of the movies. For Collaborative Filtering, I have used Matrix Factorization to generate real-time recommendations based on user-movie rating matrix. Correlation Coefficient is used to calculate the score. The higher the score, the more likely the movie is liked by the user. I have used the voting system for Hybrid Filtering. Weighted Rating Score is calculated with the help of the votes given to the movies by the users. Then a popularity feature is also used to generate more meaningful movie recommendations. MinMax Scalar is used to normalize the data, and then fifty percent priority is given to both Weighted Rating Score and popularity and a final score is calculated. Using this final score, popular or top movies are generated. The databases used are retrieved from MovieLens and Kaggle. Consequently, my proposed system will generate recommendations based on the plot of the movie using tf-idf and cosine similarity, based on user-movie rating matrix and correlation coefficient, based on voting and popularity basis.
Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions

Text segmentation tasks have a very wide range of application values, such as image editing, style transfer, watermark removal, etc. However, existing public datasets are of poor quality of pixel-level labels that have been shown to be notoriously costly to acquire, both in terms of money and time. At the same time, when pretraining is performed on synthetic datasets, the data distribution of the synthetic datasets is far from the data distribution in the real scene. These all pose a huge challenge to the current pixel-level text segmentation algorithms. To alleviate the above problems, we propose a self-supervised scene text segmentation algorithm with layered decoupling of representations derived from the object-centric manner to segment images into texts and background. In our method, we propose two novel designs which include Region Query Module and Representation Consistency Constraints adapting to the unique properties of text as complements to Auto Encoder, which improves the network's sensitivity to texts. For this unique design, we treat the polygon-level masks predicted by the text localization model as extra input information, and neither utilize any pixel-level mask annotations for training stage nor pretrain on synthetic datasets. Extensive experiments show the effectiveness of the method proposed. On several public scene text datasets, our method outperforms the state-of-the-art unsupervised segmentation algorithms.
M4MM '22

Transversely to all multimedia research, the methods and tools are often shared by several MM topics and applications. M4MM aims to foster discussion around fundamental methodologies and tools that are used in various multimedia research topics. To that aim, the technical program of the workshop consists of two keynote speakers, on complementary and very relevant methods and tools for MM, as well as four technical papers. The complete M4MM'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3552487
IXR '22

Despite remarkable advances, current Extended Reality (XR) applications are in their majority local and individual experiences. A plethora of interactive applications, such as teleconferencing, tele-surgery, interconnection in new buildings project chain, Cultural Heritage and Museum contents communication, are well on their way to integrate immersive technologies. However, interconnected, and interactive XR, where participants can virtually interact across vast distances, remains a distant dream. In fact, three great barriers stand between current technology and remote immersive interactive life-like experiences, namely the (i) content realism, (ii) motion-to-photon latency, and accurate (iii) human centric quality assessment and control. Overcoming these barriers will require novel solutions at all elements of the end-to-end transmission chain. This workshop focuses on the challenges, applications, and major advancements in multimedia, networks and end-user infrastructures to enable the next generation of interactive XR applications and services. The complete IXR'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3552483
NarSUM '22

With video capture devices becoming widely popular, the amount of video data generated per day has seen a rapid increase over the past few years. Browsing through hours of video data to retrieve useful information is a tedious and boring task. Video Summarization technology has played a crucial role in addressing this issue. It is a well-researched topic in the multimedia community. However, the focus so far has been limited to creating summary to videos which are short (only a few minutes). This workshop aims to call for researchers on relevant background to focus on novel solutions for user-centric narrative summarization of long videos. This workshop will also cover important aspects of video summarization research like what is "important" in a video, how to evaluate the goodness of a created summary, open challenges in video summarization etc.
RoBERTa: language modelling in building Indonesian question-answering systems

This research aimed to evaluate the performance of the A Lite BERT (ALBERT), efficiently learning an encoder that classifies token replacements accurately (ELECTRA) and a robust optimized BERT pretraining approach (RoBERTa) models to support the development of the Indonesian language question and answer system model. The evaluation carried out used Indonesian, Malay and Esperanto. Here, Esperanto was used as a comparison of Indonesian because it is international, which does not belong to any person or country and this then make it neutral. Compared to other foreign languages, the structure and construction of Esperanto is relatively simple. The dataset used was the result of crawling Wikipedia for Indonesian and Open Super-large Crawled ALMAnaCH coRpus (OSCAR) for Esperanto. The size of the token dictionary used in the test used approximately 30,000 sub tokens in both the SentencePiece and byte-level byte pair encoding methods (ByteLevelBPE). The test was carried out with the learning rates of 1e-5 and 5e-5 for both languages in accordance with the reference from the bidirectional encoder representations from transformers (BERT) paper. As shown in the final result of this study, the ALBERT and RoBERTa models in Esperanto showed the results of the loss calculation that were not much different. This showed that the RoBERTa model was better to implement an Indonesian question and answer system.
A Multi-Stream Approach for Video Understanding

The automatic annotation of higher-level semantic information in long-form video content is still a challenging task. The Deep Video Understanding (DVU) Challenge aims at catalyzing progress in this area by offering common data and tasks. In this paper, we present our contribution to the 3rd DVU challenge. Our approach consists of multiple information streams extracted from both the visual and the audio modality. The streams can build on information generated by previous streams to increase their semantic descriptiveness. Finally, the output of all streams can be aggregated in order to produce a graph representation of the input movie to represent the semantic relationships between the relevant characters.
Comparison of template-based and multilayer perceptron-based approach for automatic question generation system

Intelligent tutoring systems are computer-assisted learning systems with adaption to students using artificial intelligence tools. An intelligent tutoring system can drastically improve education efficiency as it provides solutions to many issues that now plague the educational industry. One important component in education is questioning learners to assess and reinforce learning. This research compares two approaches for automatic question generation, a template-based question generation strategy and the phrase-Level automatic question generation system utilizing the Multilayer perceptron model. A template-based technique is a baseline for automatic question generation that uses templates taken from the training set to generate questions by filling certain templates with specific topic items. We utilize question-answer sentence composition datasets and manually constructed datasets for our experiments and comparison of the Multilayer perceptron training model. We used both human and automatic evaluation metrics to assess the efficiency of our suggested methods. Regarding automatic metrics, we selected the BLEU-n gram and ROUGE-N methodologies. The evaluation results demonstrate that the phrase-level Multilayer perceptron-based strategy dominates the template-based approach and has a promising score in both ROUGE automatic and human evaluation metrics.
Building an efficient content based image retrieval system by changing the database structure

The &lt;span&gt;amount of interest in digital images is growing because of the advent of the World Wide Web. Many different industries are making use of digital images today, including medicine, design, journalism, education, and more. Images can be stored and retrieved using a variety of techniques and methods. Nevertheless, the majority of those engines are dependent on Meta data (key-words, descriptions, tags). Although efficient, costly, and able to target keywords and tags with each image, and these engines have their shortcomings. Though it may be possible to use a filtering system to give more accurate results, there are additional issues.&lt;/span&gt;
Present and absent keyphrases extraction: an approach based on sentence embedding

The automatic keyphrases extraction (AKE) of a document is any expression by which we can learn its content without having to read it. Keyphrases are exploited in natural language processing (NLP) applications. These phrases are often mentioned in the document but there may be some keyphrases that are not mentioned. In the field of AKE, researchers have exploited many techniques, such as statistical calculation, deep learning algorithms, graph representation, and sentence embedding techniques. Approaches that exploit embedding techniques calculate the similarity between a document and a candidate keyphrase, where similar phrases to the document are considered as keyphrases. Representing the document by a single vector makes its performance poor, especially in long documents. This is in addition to the inability of these methods to generate absent keyphrases. In order to overcome these problems, our paper proposes an unsupervised approach to AKE, based on the universal sentence encoder (USE) to represent candidate keyphrases and parts of the document probably containing keyphrases. Our method also generates keyphrases not mentioned in the text. We compared the performance of the proposed approach with other methods based on embedding techniques, where the results showed the superiority of our approach especially in long documents.
Explainable k-means. Don't be greedy, plant bigger trees!

We provide a new bi-criteria $\tilde{O}(\log^2 k)$ competitive algorithm for explainable $k$-means clustering. Explainable $k$-means was recently introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). It is described by an easy to interpret and understand (threshold) decision tree or diagram. The cost of the explainable $k$-means clustering equals to the sum of costs of its clusters; and the cost of each cluster equals the sum of squared distances from the points in the cluster to the center of that cluster. The best non bi-criteria algorithm for explainable clustering $\tilde{O}(k)$ competitive, and this bound is tight. Our randomized bi-criteria algorithm constructs a threshold decision tree that partitions the data set into $(1+\delta)k$ clusters (where $\delta\in (0,1)$ is a parameter of the algorithm). The cost of this clustering is at most $\tilde{O}(1/ \delta \cdot \log^2 k)$ times the cost of the optimal unconstrained $k$-means clustering. We show that this bound is almost optimal.
Multi robot cooperative rescue based on two-stage task allocation algorithm

Abstract Task allocation is an important issue in the decision-making of multi robot fire rescue in underground garage. In this paper, a multi robot and multi-objective optimization model is constructed. According to the real-time requirements of fire rescue in the underground garage scene, a two-stage multi robot task allocation algorithm based on ant colony and contract net protocol is proposed, which solves the global deficiency of the allocation result based on contract net protocol and the defect that ant colony optimization can not respond to environmental changes in real time. The comparative test results in dynamic environment show that the two-stage task allocation algorithm is similar to ant colony optimization in global cost, and its real-time performance is better than ant colony optimization.
Analysis and troubleshooting of fin stabilizer shutdown

Abstract Starting from an accident of overload trip and shutdown of fin stabilizer main motor caused by fin stabilizer fault, this paper studies the working principle of this type of fin stabilizer hydraulic system and electrical control system, analyzes the root cause of the fault, introduces the fault treatment method and process in detail, and puts forward suggestions on the daily maintenance and management of the equipment, so as to prevent similar accidents from happening again.
Vote algorithm based probabilistic model for phishing website detection

Internet scams have been a major concern for everyone over the past decade. With the advancement of technology, attackers have formulated different kinds of contemporary fraudulent procedures to obtain user’s sensitive information. Phishing is one of the oldest and common fraudulent attempts by which every year millions of internet users fall victim to scams resulting in losing their money. Different techniques and algorithms have been proposed by researchers in detecting phishing websites. However, the detection of phishing websites has few challenges since there are different subjective considerations and ambiguities involved in the detection process. This paper presents a two-stage probabilistic method for detecting phishing websites based on the vote algorithm. In the first stage, 29 different base classifiers have been used and their probabilistic values were calculated. In the second stage, the voting algorithm aggregated the probabilistic values of several base classifiers and the phishing websites were detected using the average of probabilities approach. The voting technique achieved an accuracy of 97.431% outperforming all of the single base classifiers in terms of accuracy.&lt;br /&gt;&lt;br /&gt;
Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

Abstract Recently, Deep Learning and blockchain technology has gained successful attention in recent research due to their high potential for generating accurate decision. The operational transparency, traceability, dependability, security, and trustworthy data provenance characteristics are offered by the vast majority of current deep learning approaches that are developed on centralized servers. Therefore, using centralized data to train Deep Learning models makes them vulnerable to the single point of failure issue. In this article, the benefits of combining Blockchain and Deep Learning algorithms are explored. Moreover, the state-of-the-art literature is reviewed to find out the most possible way to integrate these two technologies for an optimal solution. Based on the literature survey this article is divided by a thematic taxonomy that includes seven different parameters such as Deep Learning techniques, protocols, domains, types of blockchain, use of deep learning and blockchain, and datasets. We have outlined the advantages and disadvantages of the most advanced blockchain-based deep learning frameworks to give insightful discussions. At last, the most important challenges that need further research are also highlighted with future recommendations.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Edas • Number of submissions received: 191 • Number of submissions sent for review: 191 • Number of submissions accepted: 121 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 63.4 • Average number of reviews per paper: 2 • Total number of reviewers involved: 60 • Contact person for queries: Name: Ristama Simatupang Email: ristasmtp79@gmail.com Affiliation: Universitas Terbuka Indonesia
Multiple Temporal Fusion based Weakly-supervised Pre-training Techniques for Video Categorization

In this paper, we present our solution of the ACM Multimedia 2022 pre-training for video understanding challenge. First, we pre-train the models on large-scale weakly-supervised video datasets with different temporal resolutions, then fine-tune the model for downstream application. Quantitative comparisons are conducted to evaluate the performance of different networks at multiple temporal resolutions. Moreover, we fusion different pre-trained models through weighted averaging. We achieve an accuracy of 62.39% in the testing set, which ranked as the first place in the video categorization track of this challenge.
Remote switch for Schrödinger’s cat state using Einstein-Podolsky-Rosen entanglement

We propose a 'remote switch' for Schrödinger's cat state (SCS). Resorting to nonlocal correlations, we demonstrate that an approximate SCS can be heralded at one mode of an Einstein-Podolsky-Rosen entangled state, via a conditional 'hybrid projective measurement' (HPM) performed on the other one mode. The HPM is able to fully manipulate both size and parity of the generated SCS. Here, the HPM consists of both photon number measurement and homodyne conditioning. Such a remote switch for SCS will open up new ideas in subsequent protocols, including fundamental tests and nonlocal manipulation of non-Gaussian states.
Rethinking the Metric in Few-shot Learning

Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the relationship among different distance metrics. In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification. We start from a naive baseline of confidence summation and demonstrate the necessity of exploiting the complementary property of different distance metrics. By finding the competition problem among them, built upon the baseline, we propose an Adaptive Metrics Module (AMM) to decouple metrics fusion into metric-prediction fusion and metric-losses fusion. The former encourages mutual complementary, while the latter alleviates metric competition via multi-task collaborative learning. Based on AMM, we design a few-shot classification framework AMTNet, including the AMM and the Global Adaptive Loss (GAL), to jointly optimize the few-shot task and auxiliary self-supervised task, making the embedding features more robust. In the experiment, the proposed AMM achieves 2% higher performance than the naive metrics fusion module, and our AMTNet outperforms the state-of-the-arts on multiple benchmark datasets.
Problematika Penggunaan Website Learning SIMPeL 12 Terhadap Hasil Belajar Geografi di SMA Negeri 12 Semarang Tahun Ajaran 2020/2021

SIMPeL 12 as website learning was used at SMA Negeri 12 Semarang. The aims of thus study are: (1) To find out the problems of using the SIMPeL 12 learning website in geography subjects at SMA Negeri 12 Semarang. (2) Know the result of studying geography at SMA Negeri 12 Semarang. (3) Determine the effect of problematic use of the SIMPeL 12 learning website on the learning outcomes of geography at SMA Negeri 12 Semarang. The method used in this research is descriptive quantitative with data collection using questionnaires, interviews, and documentation. The analysis technique used is descriptive statistics and simple regression. The result of this study indicate that (1) the problems of using the SIMPeL 12 learning website in geography subject get an average criterion of “High” with a percentage of 68,1%, caused by internal factors consisting of interest and readiness, motivation, and family. While external factors consist of access and flexibility, facilities and infrastructure, cost, and geography subject matter. (2) the result of the study of geography showed that as many as 42 samples did not reach the Minimum Completeness Criteria. (3) the effect of the problematic use of the SIMPeL 12 learning website on the learning outcomes of geography at SMA Negeri 12 Semarang is 25%.
KENDALA DALAM PEMBELAJARAN DARING BAGI SISWA PADA MATA PELAJARAN GEOGRAFI DI SMA NEGERI 16 SEMARANG

This study aims to determine the obstacles experienced by students during online learning activities in geography subjects at SMA Negeri 16 Semarang. The population in this study was the social studies class at SMA Negeri 16 Semarang. The sample of this research is class XI IPS 1 with 36 students taken using cluster random sampling technique. Methods of data collection using a questionnaire or questionnaire. The data analysis technique used descriptive statistics. Based on the result of this study, it was found that: online learning activities showed a percentage value of 86% in the poor category. After being researched, it turns out that there are obstacles in digital literacy activities, discussions, evalutions, and learning facilities. Therefore, it can be concluded that online learning activities are not running optimally.