Reviewerly/exHarmony huggingface dataset:
CLS-Net: An Action Recognition Algorithm Based on Channel-Temporal Information Modeling

The modeling of channel and temporal information is of crucial importance for action recognition tasks. To build a high-performance action recognition network by effectively capturing channel and temporal information, we propose CLS-Net: an action recognition algorithm based on channel-temporal information modeling. The proposed CLS-Net characterizes channel and temporal information by inserting multiple modules to an end-to-end backbone network, including a channel attention module (CA module) for modeling channel information, a long-term temporal module (LT module) and a short-term temporal module (ST module) for modeling temporal information. Specifically, the CA module extracts the correlation between feature channels so the network can learn to selectively strengthen the features containing useful information and suppress the useless features through global information. The LT module moves some channels in the temporal dimension to realize information interaction across time domains and model global temporal information. The ST module enhances the motion-sensitive features by calculating the feature-level frame difference information and realizes the representation of local motion information. Since the multi-module insertion mode directly affects the whole model’s final performance, we propose a novel multi-module insertion mode instead of a simple series or parallel connection to ensure that the multiple modules can complement one another and cooperate with each other more efficiently. CLS-Net achieves SOTA performance on the EgoGesture and Jester dataset in the same type of network and achieves competitive results on the Something-Something V2 dataset.
Werner states from diagrams

We present two results on multiqubit Werner states, defined to be those states that are invariant under the collective action of any given single-qubit unitary that acts simultaneously on all the qubits. Motivated by the desire to characterize entanglement properties of Werner states, we construct a basis for the real linear vector space of Werner invariant Hermitian operators on the Hilbert space of pure states; it follows that any mixed Werner state can be written as a mixture of these basis operators with unique coefficients. Continuing a study of "polygon diagram" Werner states constructed in earlier work, with a goal to connect diagrams to entanglement properties, we consider a family of multiqubit states that generalize the singlet, and show that their 2-qubit reduced density matrices are separable.
<i>SpatDistCalib</i>: a GUI Python software for spatial-distortion correction of 2D detectors using splines

CCD-based X-ray detector systems often suffer from spatial distortions. Reproducible distortions can be quantitatively measured with a calibration grid and described as a displacement matrix or as spline functions. The measured distortion can be used afterwards to undistort raw images or to refine the actual position of each pixel, e.g. for azimuthal integration. This article describes a method using a regular grid, not necessarily orthogonal, to measure the distortions. The graphical user interface (GUI) Python software that is used to implement this method is available under a GPLv3 license on ESRF GitLab, and produces a spline file that is usable with data-reduction software such as FIT2D or pyFAI.
CYBERSECURITY AND FUSION CENTERS

AbstractAbstractThe complex landscape of cybersecurity demands a simplified approach to facilitate a better understanding of potential risks and threats. Cybersecurity professionals can achieve this through the establishment of a Cybersecurity Security Operations & Fusion Center (CSOFC), which combines the proven capabilities of Security Operations Centers (SOCs) and Fusion Centers. This centralized hub provides a cohesive entity responsible for monitoring, detecting, protecting, and raising awareness of present and resolved cyber threats. The integration of Threat Intelligence Center (TIC) processes and techniques within the CSOFC framework significantly enhances the overall effectiveness of cybersecurity operations. By incorporating TIC methods and Fusion Center integration into the SOC, organizations can better anticipate potential threats and implement proactive measures to protect their digital assets. The key to making this work is integration, which relies on seamless collaboration among various teams and the utilization of state-of-the-art technologies designed for threat detection, analysis, and remediation. The establishment of a CSOFC is a significant step in simplifying the complex landscape of cybersecurity, allowing organizations to take a more proactive and comprehensive approach to cyber defense. Disclosure statementNo potential conflict of interest was reported by the author(s).
A Publicly Verifiable Optimistic Fair Exchange Protocol Using Decentralized CP-ABE

Abstract Fair exchange is a challenging problem for two mutually distrusting players. It is widely known that fair exchange is impossible without a trusted third party (TTP). However, relying on a single TTP can cause a single-point failure. An intuitive idea is to adopt multiple TTPs to distribute trust. This paper constructs a two-party optimistic fair exchange (OFE) protocol using decentralized ciphertext-policy attribute-based encryption (CP-ABE), achieving decentralized TTPs. This is achievable because decentralized CP-ABE ciphertext supports a nested access control policy. A nested access control policy fits perfectly in a fair exchange protocol which contains multiple roles (i.e. players and TTPs). Further, we apply non-interactive zero knowledge proofs to prove the well-formedness of ciphertexts, so as to enforce players to follow the protocol specification honestly. Consequently, we construct an OFE protocol in which each player’s operations are publicly verifiable without revealing secret information. Also, we obtain decentralized TTPs with optimism (i.e. the TTPs are involved only when arbitration is required), autonomy (i.e. the TTPs do not need to interact with each other), statelessness (i.e. the TTPs do not need to store data for the exchange protocol) and verifiability (i.e. the TTPs are publicly verifiable). Compared with previous work, our protocol assumes only a public communication channel and each party’s operations are publicly verifiable. Besides, it achieves a favorable $O(n)$ verification complexity in the normal case, where $n$ is the number of TTPs. Finally, we present a proof-of-concept implementation to demonstrate the feasibility.
Learning power Gaussian modeling loss for dense rotated object detection in remote sensing images

Object detection in Remote Sensing (RS) has achieved tremendous advances in recent years, but it remains challenging for rotated object detection due to cluttered backgrounds, dense object arrangements and the wide range of size variations among objects. To tackle this problem, Dense Context Feature Pyramid Network (DCFPN) and a power α-Gaussian loss are designed for rotated object detection in this paper. The proposed DCFPN can extract multi-scale information densely and accurately by leveraging a dense multi-path dilation layer to cover all sizes of objects in remote sensing scenarios. For more accurate detection while avoiding bottlenecks such as boundary discontinuity in rotated bounding box regression, α-Gaussian loss, a unified power generalization of existing Gaussian modeling losses is proposed. Furthermore, the properties of α-Gaussian loss are analyzed comprehensively for a wider range of applications. Experimental results on four datasets (UCAS-AOD, HRSC2016, DIOR-R, and DOTA) show the effectiveness of the proposed method using different detectors, and are superior to the existing methods in both feature extraction and bounding box regression.
Functional clustering methods for binary longitudinal data with temporal heterogeneity

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.
Community-based social recommendation under local differential privacy protection

Social recommendation refers to recommendation technology taking social relations as additional input to improve merchandise sales and user satisfaction. It has been widely used in various social networking services. Social recommendation requires the collection of each user's social relations to build a social graph, posing the risk of user privacy intrusion caused by untrusted servers. Local differential privacy is a widely adopted approach for providing privacy protection while allowing acceptable utility of the protected data for analytics. Growing research interest has been applying local differential privacy protection to social graph analysis. However, local differential privacy is suitable for analyzing coarse-grained statistics from perturbed user data, and it is difficult to obtain the fine-grained user relations needed for social recommendation. This paper proposes a novel locally differentially private social recommendation method. It first perturbs the degrees of users in a given interval and discovers coarse-grained communities from them, then generates a fine-grained social graph according to the features of intra-community and inter-community relations, thus ensuring the accuracy of the recommendation results. Experimental results show that the social recommendation accuracy of our proposed method improves by about 20% compared to existing methods in a community-structured ego-network dataset, and by about 10% in two non-community-structured page-page network datasets. The experimental results show that compared with the state-of-the-art methods such as LDPGen, LFGDPR, and AsgLDP, the social recommendation accuracy of our proposed method is improved by about 20% in one community-structured ego-network dataset, and 10% in two non-community-structured page-page network datasets.
Transition of Public and Academic Library Makerspaces from Physical to Online Settings during a Pandemic

This research investigates how academic and public library makerspaces transitioned to online maker programming during COVID-19. Using a collective case study methodology based on interviews with library staff, the findings describe three resources that supported the online transition—existing knowledge, technology infrastructure and staff with technology expertise, and interorganizational collaboration—while navigating challenges related to logistics, lack of staff and training, substitution for the benefits of experiential learning, facilitation of online interactions, online marketing and program assessment, and social issues. The findings illustrate how online maker programming has expanded through changes in approaches. This article illustrates an increased level of accessibility, new skills for future maker programming, and changes in perceptions about online learning and making, as three central outcomes. These findings lay the groundwork for future research developing robust design guidelines for online maker programming beyond the pandemic.
Bounded-collusion decentralized ABE with sublinear parameters

In this paper, we propose a decentralized ABE scheme against bounded collusion which means the number of users in the system is a-prior bounded. The scheme enjoys public key and ciphertext of sublinear sizes in the number of users in the system while all prior constructions require linear sizes. Besides, our scheme achieves semi-adaptive security under bilateral k-Lin assumption and SXDH assumption in a pairing group. Keep the same as the previous constructions, the scheme supports monotone span program as a policy and does not rely on the random oracle. Technically, we follow Wang et al.'s "linear secret sharing scheme (LSSS) + inner-product functional encryption (IPFE)" paradigm [PKC'19] and use (an extended variant of) functional encryption for quadratic functions (QFE) in the place of IPFE. By this, we encrypt with sublinear-size random coins and later expand them to linear-size entropy for security proof. Roughly, the use of QFE requires bilateral k-Lin assumption while the entropy expansion relies on SXDH.
Online scheduling of coflows by attention-empowered scalable deep reinforcement learning

With the abstraction of parallel data transmission flows being a coflow, data transmissions in large-scale computing jobs can be modeled by a coflow directed acyclic graph (coflow DAG) in which nodes are coflows and edges represent dependencies between coflows. Efficient scheduling of coflows on network links is crucial for reducing the overall communication and job completion time. The known best coflow scheduling method deploying deep reinforcement learning (DRL), DeepWeave (Sun et al., 2020), suffers from poor scalability due to the requirement of O(dn)-size policy network for processing n coflows of d dimensions which is difficult to train. This paper extends the directed acyclic graph neural network (DAGNN) to Pipelined-DAGNN that embeds the features of different stages of input coflow DAGs in pipeline to effectively speed up the feature extraction process. To effectively process the feature vectors of coflow DAGs of arbitrary size and shape without compromising scheduling accuracy (quality), we propose a novel self-attention empowered DRL coflow scheduling model to generate coflow scheduling policies, which enables the scale of policy network depends only on features (dimensions) rather than coflows, without the need of packing all individual embedding vectors from Pipelined-DAGNN into a long flat vector. Our model reduces the size of the policy network in DRL from previously O(dn) to O(d), achieving a high scalability independent of the number of coflows. Simulation results on Facebook trace show that our model reduces the average weighted job completion time by up to 33.88%, apart from being more scalable and robust, compared with the state-of-the-art methods.
Color object classification using multi-channel Zernike moments-based rotation invariant bag-of-visual-words and deep convolutional neural networks

We propose two rotation-invariant approaches for color object classification and recognition using multi-channel Zernike moments (MZMs)-based bag-of-visual-words (BoVWs) and deep convolutional neural networks (DCNN). The first approach, referred to as MZMs-BoVWs, derives the BoVWs descriptors from a color image and its sub-images after forming its hierarchical sub-divisions and computing the MZMs from each of the sub-images to determine similarity among sub-images to facilitate the construction of BoVWs. The second approach, called DCNN-MZMs, derives the MZMs descriptors from the feature maps of a convolutional layer of a DCNN architecture designed in this paper. A fusion of the descriptors, MZMs-BoVWs and CNN-MZMs, along with the high-performing color histograms (CH) descriptor, i.e., CH+MZMs-BoVWs+CNN-MZMs, is also proposed. The process of the object classification is performed using SVM and its multiple-kernel learning approach. Experimental results show that the proposed approaches outperform the existing state-of-the-art approaches under image-rotation and under limited data for training the CNNs.
A Study of the Compilation of Selected Poems, Cheonggusicho and Its Meaningin Korean Chinese Literature History

The main purpose of the present study is to elucidate the background and process in the compilation of selected poems, Cheonggusicho published by the Japanese Government-General of Joseon and its meaning in Korean Chinese literature history. Because Kim, Taejun made a diachronic study of Korean Chinese literature history to describe ancient parts of Joseon Chinese Literature History by using a lot of Cheonggusicho.&#x0D; The Competitive Exhibition of Local Products for the Fifth Anniversary of New Politics’ held by the Japanese Government-General of Joseon provided an occasion to publish Cheonggusicho. It was held to commemorate the fifth year of Japanese rule of Joseon and to make Joseon merged as a colony of Japan.&#x0D; Though Cheonggusicho was published to commemorate the fifth year of Japanese rule of Joseon, it was not a local product. It seems that its aim was for some Japanese to enjoy Korean folk culture, due to the words in the preface, “to give the books printed by the typographic culture of Joseon to the ‘persons interested in the same subject’”. It is doubtful why Cheonggusicho was taken to Joseon Chinese Literature History.&#x0D; Cheonggusicho consists of 176 poems stemmed from Gojoseon, the first Korean kingdom, to the Goryeo dynasty. Over 130 poems in it were recorded on Dongmunseon. The poems representing each historical era are arranged in chronological order with a poem a poet. It is different from other anthologies. So the origin of Joseon poetry dates back to the earlier era.&#x0D; According to the preface of Joseon Chinese Literature History, it does not belong to Chinese Literature because it is the translated Chinese character version of native Korean words. At that time when Kim, Taejun wrote Joseon Chinese Literature History, Samgukyusa(Memorabilia of the Three Kingdoms) began to spread and the literary approach to Hyangga(old Korean folk songs), Korean myths and folktales emerged in the academy. That Chinese Literature in Korea should belong to Joseon Chinese Literature History was under discussion.&#x0D; In conclusion, though Cheonggusicho was published for some Japanese to enjoy Korean folk culture, it is a clue to spread the origin of old Korean literature by Kim, Taejun at that time when the concept of literature began to change.
Virtual Mouse using Coordinate Mapping

Recent advances in technology have enabled the development of systems that allow users to control their computers with hand movements, or gesture-based controls. Despite the progress made in this area, there remain challenges associated with accurately tracking and interpreting hand movement in order to control the computer. This paper presents an overview of the current state of the art in virtual mouse systems, which use cameras to track and interpret hand gestures, and convert them into mouse movements. This study discusses the challenges associated with this approach, and provide a review of recent techniques used to improve the accuracy and reliability of virtual mouse systems. Furthermore, the proposed objective of creating an advanced virtual mouse system is presented that can accurately track and interpret hand movements in real-time with minimal user effort. This paper provides a detailed explanation of methodologies used in virtual mouse.
Robotization on Solar Panel Installation: Location Marking on Steel Roof

Solar power is exploited from the heat and power of the sun rays. It is a renewable type, and so called a green source of energy. The habitual method of exploiting the energy from the sun is by using solar panels. The installation of solar panels on roofs is a time-consuming operation that requires a significant amount of manual work. Since the impression of daylight on the rooftop causes the sheet surface to warm up so much, denoting the solar panel position in the rooftop is generally done in the first part of the day or late around evening time. Subsequently, the time required basically deciding the area is extensive, delay the establishment cycle. The mechanical bot has been intended for denoting the area of establishment of solar panel on the rooftop in this proposed thought, which brings down the requirement for human association and paces up the establishment method.
Development of Inertial Acceleration Measurement Device with Zero-Compliance Mechanism

Accelerometer using zero-compliance mechanism was studied both analytically and experimentally. In the originally conceived device, an inertial mass is suspended by zero-compliance mechanism that consists of a positive-stiffness suspension and a negative-stiffness suspension with same amplitude of stiffness. They are connected in series at the detection point. When the device is accelerated, the relative displacement of the inertial mass is kept zero while the detection point displaces. The amplitude of acceleration equals the product of the stiffness and the displacement of the detection point divided the inertial mass. A measurement device was fabricated to investigate basic characteristics of the proposed accelerometer. In this work, analytical studies supporting the validity of the acceleration measurement are shown. Several results of measuring fine acceleration are also presented.
Shield of White-Collar Crime Through the Intervention of AI, IoT and Blockchain

White collar crime, such as fraud, embezzlement, and insider trading, often goes undetected and unpunished, creating a perception of impunity and undermining public trust in institutions. These crimes can result in financial losses, harm to reputation, and contribute to economic instability. Despite this, there is limited research on the use of Industry 4.0 technologies, such as the Internet of Things, Blockchain, and Artificial Intelligence, to prevent white collar crime. This study aims to address the gap by examining the challenges of development and application of IoT, AI and Blockchain technologies by the governmental agencies. Specifically, the study suggests that IoT and AI can be used to develop robust, generalizable models for detecting and preventing white collar crime, and that security and privacy measures must be implemented to protect the data collected from IoT devices and prevent misuse of personal data.
Methodology for the design of low cost medical devices for screaning use: audiometer case

The recent technological development results in developing new devices for different uses, such as applications of various uses, which can be used for many applications. This study demonstrated a novel methodology to conFigure a digital audiometer using generic devices such as the use of high-end headphones in video games, and mobile applications known as function generators, with which an audiometer can be configured and through mobile devices, the signal values can be generated and sent to the headphones configured by a protocol frequencies and audio levels. As a result, this study presents a configuration, using headphones of the Razer high definition video game line, with a generic application for generating functions, which allows configuring the independent output to each headphone, such as frequency and audio level. The method can be applied and scaled towards the use as a quick analysis audiometer, as an initial evaluation mechanism, to identify possible patterns related to auditory behavior; this cannot replace the medical grade equipment.