Reviewerly/exHarmony huggingface dataset:
Pyramid Knowledge Distillation for Efficient Human Pose Estimation

Human pose estimation is an important task in many real-time applications. Existing methods directly slim the CNN by deploying well-designed lightweight modules. However, these methods lack privileged information guidance and the knowledge distillation technique stays less explored. In this work, we propose a novel method, namely Pyramid Knowledge Distillation (PKD) for efficient human pose estimation. Specifically, PKD composes of Pyramid Structured Map Distillation (PSMD) and Pyramid Feature Map Distillation (PFMD). In PSMD, we formulate a structured map encoding robust interjoint correlation. Based on structured map, the spatial dependencies between keypoints can be better transferred from a cumbersome teacher network to a compact student model. To further promote the efficiency of student, PFMD is used to distill rich local and global features from teacher. Experiments demonstrate that PKD achieves an optimal trade-off between cost and accuracy on COCO and MPII benchmarks, even with a much faster inference speed.
Low-Complexity Scaler Based on Convolutional Neural Networks for Adaptive Video Streaming

Adaptive streaming service nowadays, became an essential key technology for delivering videos over internet protocol network. Due to limitations and fluctuations in internet bandwidth, scaler has become essential for streaming service. Recently, learning-based scalers have greatly improved performance compared to conventional methods in visual quality. However, performance is not guaranteed for compatibility of conventional scaler and real-time processing is difficult due to high complexity. In this paper, we present a low complexity scaler based on convolutional neural networks called Video Scale Network (VSN). The proposed method has a simple structure with real-time processing and a loss function compatible of conventional scaler. Furthermore, we propose a learning method to improve performance using only a single network. Experimental results on the AOM test sequences reveal improved performance by using the proposed method compared to the conventional method and real-time processing in the decoder of AV1 codec is also able to be done.
Dynamic Selection Network For Rgb-D Salient Object Detection

Existing RGB-D salient object detection (SOD) methods usually use elaborate fusion modules for exploring cross-modal information, which is computationally expensive and ignores the noise depth information. To deal with this issue, we propose a dynamic selection network (DSNet) for RGB-D salient object detection. Specifically, a cross-modal combination module (CCM) is proposed to fuse two modalities with a light computation. Then a dynamic selection module (DSM) adaptively learns the model parameter for the decoding based on the fused features. Furthermore, skip connection is used for hierarchical features combination between encoder and decoder. Experiments on four popular datasets demonstrate our model outperforms other state-of-the-art methods.
A Scalable Hardware Circuit for Factoring an n-bit Integer: Analytic and Computational Perspectives

This chapter addresses a prominent problem that is ubiquitous in scientific and engineering applications including the usually challenging task of cryptanalysis. This problem is the problem of integer factorization, i.e., the decomposition of a composite integer into a product of smaller integers, restricted herein to be prime integers. This is an intractable problem that might admit real-time hardware solutions for small bit sizes. This chapter suggests manual and automated scalable solutions for integer factorization based on equation solving over big Boolean algebras. The manual solution is illustrated over a form of 8-variable Karnaugh maps that is highly regular and modular. This solution covers the problem of 6 bits, which includes the problems of 5, 4, and 3 bits as special cases. Additionally, an automated solution is used, and the findings are then shown and briefly explained. These findings demonstrate the well-known evolution of temporal and spatial complexity with increasing input bit count. In future work, the largest possible hardware circuit that could be obtained via the automated solution is to be constructed, verified and tested. The complexity in our solution comes from two sources. One involves the task of finding the Boolean expressions for the solution. This task is slow, but it has to be done once, and only once, for a problem of a given size. The hardware implementation (e.g., an FPGA implementation) could serve as a ready real-time look-up solution not only of the pertinent problem but also of all smaller problems.
Demo -- MaLFraDA

Training machine learning algorithms on sensitive, illegal to possess, and psychologically harmful data is challenging because researchers have to do training without handling the data. Moreover, the nature of the data imposes strict control, monitoring, and examination of all the activities involved, including communication, execution, and release of algorithms, datasets, outputs, and results. In this regard, this work proposes a new multi-zoned framework called MaLFraDA. MaLFraDA has soft air gaps between its zones to isolate and control communication in and out of the framework. Besides, it includes (i) a vetter to investigate and approve incoming model/algorithm, and outgoing information, (ii) encrypted data vaults, and (iii) airlock instances for secure execution/computation. MaLFraDA, with an extension, runs popular distributed machine learning algorithms such as federated and split learning using multiple data custodians.
Cyber security control systems for operational technology

The capacity to consequently coordinate cyber threat intelligence into your security framework for proactive insurance is “Robotized DNS Sinkhole Breach Detection.” Security mechanization abilities by locally incorporating cyber threat intelligence into “Computerized Penetration Testing” and “Robotized Incident Response” responds to improved break identification and examination. Coordinated security frameworks are the most ideal alternatives for any business. In addition to the fact that they offer the best improvements, they will guarantee every one of your bases are covered. Modern frameworks were brought online to convey huge information and shrewd investigation just as they acquire new abilities through innovative incorporations. Modern conditions generally have lower signal traffic than IT conditions. A significant part of the traffic signal is going between resolved endpoints and subsequently can be baselined and stocked highly effectively than traffic that is produced in an IT organization. The harms from an information penetration can be huge for any association.
An optimal more than one stage (MTOS) authentication model to ensure security in cloud computing

Cloud computing is one of the emerging technologies that provides the applications to process through the internet. The business world utilizes the reliability and availability of heterogeneous computing resources at a minimum cost. Anyway, it is very important to know the security of a process in the cloud environment, which provides the computing environment. Information security plays a vital role in cloud challenges where the users access the shared information through the cloud. So the major objective is to implement an efficient authentication technique to maintain information security and protect the information from illegal users or attackers from the outside world. This research work depicts the security problems of one-stage authentication and the issues of the using one password. In this regard, a novel security mechanism based on more than one stage (MTOS) authentication has been proposed in the cloud environment. The proposed work contains tristage of authentication and the information will be bisected among these stages based on the susceptibility to privilege (P), secret (S) and high secret (HS). The information at the stage P has the minimum susceptibility because the users make use of only one password to access this information stage. Two passwords such as texts and fingerprint are used to access the current stage and minimum stage. The user at stage HS uses three passwords such as texts, fingerprints and vision intelligence. The RSA algorithm has been used in the proposed approach for encryption purposes after the information is stored in the cloud environment. The results of the proposed system have been compared with the existing systems and this provides an optimal solution.
Poster

Tor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.
New Approach of Connectivity

This is a new concept in networking on many points. This paper shows how many ways we can network one point to another.
Multiclass voice commands classification with multiple binary convolution neural networks

In machine learning, in order to obtain good models, it is necessary to train the network on a large data set. It is very often a long process, and any changes to the input dataset require re-training the entire network. If it is necessary to extend the model with new output classes, the use of the existing model becomes problematic, and in the case of extension with new decision classes, it is required to re-train the entire model based on all data. To improve this process, a new neural network architecture was proposed, which allows for easy extension of the already existing models with new classes, without the need to re-train the entire network, as well as the time needed to train the sub-model is much shorter than the time needed to re-train the entire neural network. The presented network architecture is designed for data that has at least two decision classes.
Envisioning Digital Sanctuaries: An Exploration of Virtual Collectives for Nurturing Professional Development of Women in Technical Domains

Work and learning are essential facets of our existence, yet socio-cultural barriers may limit access and opportunity in such contexts. Historically, women have faced multiple restrictions that have hindered their entry into professional ventures, many of which have also plagued the technical domains of Information technology and Computer science. Hence, it is essential to investigate how women can subvert such structural subjugation and can find channels through which they can seek support, and guidance and develop collective endurance to navigate their careers. In this regard, virtual communities (e.g., Reddit) have emerged as crucial spaces that allow community building, and opportunities for mobilizing collective action and have also emerged as sanctuaries for support and companionship. Thus, this proposed dissertation aims to focus on how these spaces are used and adopted by women for resilience building and navigating goals, aspirations, and tensions in crafting their professional journey in technical domains. The layers of analysis will include understanding the present conversational patterns in three subreddits dedicated to professional development, and design exploration to better unpack how such channels can be envisioned in the future to more deeply align the affordances of such channels with the values of these virtual spaces. The long-term goal for this work is to address the way in which such channels can be designed and curated to offer spaces for enrichment, empowerment, and advocacy with a focus on professional development for women, especially for those engaged in technical domains.
Stance Detection using Two Popular Benchmarks: A Survey

The task of extracting knowledge from text is a significant process of many intelligent applications. Stance detection is one of the strongly coming learning-based objectives. Especially, on social media (SM) available huge text. Since SN content is increasing massively every second. The tasks of stance detection (StD), like any learning-based process, need to train and test on a robust pre-made dataset benchmark. The English language has the most available benchmarks sources. Occasionally, an StD researcher needs to gain deep knowledge about these researches involving a particular domain before establishing research scope and objectives. This study aimed to help StD researchers in this emerged field; i.e. StD. A focused review on a specific domain is presented. Two StD benchmarks on English are determined as the domain of StD surveyed papers. StD Challenges and problems are introduced beside the StD models investigation. This survey’s first goal is a point to the stance detection concept and the two popular benchmarks. The second is to discuss StD involved models and managed problems.
Seung-ee and Kkaebi: A VR-Mobile Cross Platform Game based on Co-Presence for a Balanced Immersive Experience

A growing number of VR games are published in the market as head-mounted devices (HMD) become more widespread. However, most VR games are targeted for a single-player audience, and cross-platform VR experiences where multiple players are engaged have yet to be fully explored. In this paper, we propose a VR-mobile cross-platform game based on traditional Korean mythology, Seung-ee & Kkaebi. Our goal was to create an immersive and enjoyable experience for players for both mobile and VR players where they are physically co-located, increasing both co-presence and social interaction. Our game design focused on asymmetric competition and maximized the interactions between both platforms. The VR player plays the role of Jangseung (Seek), a Korean traditional totem pole, whereas the mobile user plays the role of Dokkaebi (Hide), a Korean mythological creature. Each platform has its unique skill set to interrupt each other to win the game, thus creating a highly immersive and co-present experience for both players.
Experimental acoustic modal analysis of a tenor saxophone

This paper presents an application of the Experimental Acoustic Modal Analysis (AMA) on a vintage 10 M Conn tenor saxophone. This technique originates from the traditional Modal Analysis which is commonly adopted to determine the dynamic behaviour of solid structures. The methodology is based on the measurement of Frequency Response Functions (FRFs) as a ratio of the acoustic pressure, measured by means of a set of microphones positioned along the saxophone body, and the volume velocity produced by an acoustic source and measured at the instrument input. The microphones are housed on the stripped saxophone keys by means of tailored three-dimensional printed adapters. The AMA makes use of the FRFs to extract modal parameters, such as resonance frequencies, loss factors, and mode shapes. These parameters pertain to the cavity modes of the saxophone passive resonator. The analysis has been performed for three different notes (B ♭, B, and C) and for two registers of the instrument in order to show the potential of the technique. Moreover, the influence of the mouthpiece volume on the cavity modes has been shown. The information obtained by AMA can find useful application in the validation of analytical or numerical models of this kind of musical instruments.
Optimal superposition enhancement of a mixed state by stochastic diagonal superposition-free operations

The resource theory of superposition is a generalization of coherence theory. A rigorous framework for quantifying superposition of linear independent states has been given by previous studies. In this paper, the optimal superposition enhancement of a mixed state is studied. Based on the l1 measure of superposition the optimal superposition enhancement value of a mixed state can be obtained under the stochastic diagonal superposition-free operations(or incomplete diagonal superposition-free operations). In addition, the optimal probability corresponding to the optimal superposition enhancement value of a mixed state is also discussed. The results show that although the superposition-free operations can decrease the superposition of a state, the superposition enhancement of a state is possible, a single mixed state can be probabilistically transformed into another state with higher superposition than before by superposition-free operations. These studies will give us a better understanding of the superposition manipulation and the resource theory of superposition.
Resolving Referential Ambiguity for Nouns and Verbs Across Situations by Foreign Language Learners

The study examined the process used by foreign language learners in resolving referential ambiguity across situations. Two hundred learners of English as a foreign language took two cross-situational word learning tasks, one on nouns and the other on verbs. In each trial, participants heard a novel word, observed two referents (animals or dynamic events), and selected one referent under uncertainty. Following zero or two intervening trials, they were tested by one of three probes, which preserved the word-referent mapping previously selected, switched the referent previously selected, or switched the form previously heard. Learners performed above chance on all the probes, indicating that they tracked the unselected but potential word-referent mappings across situations for later learning. The performance pattern was similar for nouns and verbs, though verb learning was slightly impaired when the referent was switched over intervening trials. Moreover, verb learning was associated with phonological short-term memory, but not with statistical learning and English vocabulary. Noun learning was associated with none of the learner variables. These results suggest that foreign language learners track all the word-referent co-occurrences across situations in resolving referential ambiguity, though success of tracking can be constrained by the complexity of the co-occurrences and individual learners’ memory capacities.
Tree‐Based Methods for Statistical Learning in R. Brandon M.Greenwell(2022). Boca Raton, FL: CRC Press. 404 pp., £59.99. ISBN 978‐0‐367‐53246‐8

Biometrical JournalVolume 64, Issue 8 p. 1500-1501 BOOK REVIEW Tree-Based Methods for Statistical Learning in R. Brandon M. Greenwell(2022). Boca Raton, FL: CRC Press. 404 pp., £59.99. ISBN 978-0-367-53246-8 Jianchang Hu, Corresponding Author Jianchang Hu [email protected] orcid.org/0000-0002-0960-1043 Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany Correspondence Email: [email protected]Search for more papers by this author Jianchang Hu, Corresponding Author Jianchang Hu [email protected] orcid.org/0000-0002-0960-1043 Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany Correspondence Email: [email protected]Search for more papers by this author First published: 04 November 2022 https://doi.org/10.1002/bimj.202200286Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume64, Issue8Special Issue: Clinical biostatistics in the 2020s (ISCB 2020)December 2022Pages 1500-1501 RelatedInformation
Modeling tool for managing canvas-based models traceability in ML system development

Analysis of machine learning models often used canvas-based models such as ML Canvas and AI Project Canvas to facilitate rapid brainstorming of ideas. However, those models often cover only high-level descriptions of requirements. Developers may utilize other models to achieve a more comprehensive analysis to cover specific aspects. This condition may lead to inconsistencies between different models. This study proposes a tool to support traceability between canvas-based and other models. The tool is implemented as a plugin for astah* System Safety.
A value-based goal model analysis tool

Goal-oriented analysis techniques analyzes goal models in order to help analysts making decisions about the system to be developed. These analyses can, for example, identify conflicts of interest among stakeholders or prioritize their goals. Although many techniques of this type have been proposed, the vast majority of them focus on the concept of satisfaction, resulting in a value-neutral approach. This means that all the elements in a goal model (e.g., stakeholders' goals) are considered to be equally important. Moreover, almost all these techniques use either a qualitative or a quantitative approach to assign weight or importance to the elements in a goal model.
A Case Study on the ‘Consumer Studies’ Class Using Problem-Based Learning for Prospective Home Economics Teachers

본 연구의 목적은 학습자 중심 교수방법인 문제기반학습(PBL)을 ‘소비자학’ 교과목에 적용하는 것이다. ‘소비자학’을 수강하는대학생 27명을 대상으로 경제개념 특강안과 재무컨설팅을 제안하는 2가지 PBL 문제를 개발하고 총 7주간 PBL 수업을 진행하였다. PBL 효과는 학습자들이 작성한 성찰일지, 자기평가지, 수업만족도를 활용하여 분석하였다. 학습자는 문제해결 과정에서 소비자학 교과목의 학습목표를 달성했으며, 팀원과의 협업과 소통 방식을 습득하는 경험을하였다. 이러한 과정에서 자연스럽게 자기주도적 학습이 이루어졌으며, 문제해결능력이 향상된 것으로 나타났다. 또한 학습자는학습과제이행, 적극적 상호작용, 자기주도적 학습수행 능력 등 PBL 수업에 임하는 스스로를 평균 4.68로 높이 평가했으며, PBL 수업에 대한 수업만족도도 평균 4.75로 매우 높게 나타났다. 이상의 결과는 교수자 및 예비교원에게 PBL 수업에 대한유용한 정보로 제공될 것이며, 학습자중심 PBL 수업을 위한 자료로 활용될 것이다.This study aims to apply the problem-based learning method to a college course on ‘Consumer Studies’ in a teacher education program. The participants of this study were 27 undergraduate students who were enrolled in the course. The PBL class was implemented for seven weeks using two problems: proposing special lectures on economic concepts and financial consulting. The effectiveness of PBL was analyzed through self-reflective journals, self-assessment, and a learning satisfaction survey of students. The result of this study was as follows. Students achieved the learning objectives and strengthened their collaboration and communication skills with team members during the PBL process. Moreover, students improved their self-directed learning and problem-solving ability through the PBL class. The results of self-assessment, in terms of learning task performance, active interaction, and self-directed learning were generally high at 4.63 points. In addition, the overall learning satisfaction level was very high, with a score of 4.75. The results will provide effective information on PBL classes to instructors and prospective teachers and will be used as data for learner-centered PBL classes.