Reviewerly/exHarmony huggingface dataset:
KeypointMask: A Real-Time Instance Segmentation for Oblique Object Detection in Robotic Picking

In vision-based robotic grasping, instance segmentation locates the object in the 2D image, and cropping the 2D mask area points cloud to estimate the object pose. The latest deep learning algorithm has a corner case and is ignored by the current state-of-the-art instance segmentation methods. In the eye-to-hand robotic picking scenario, the 3D camera is mounted on the top of the robotic arm and captures the color and depth image in the bird's eye view manner. In this situation, the intra-class suppression between the same object causes a serious missing detection issue for diagonally arranged or stacked objects with axis-aligned bounding boxes. This paper proposes a real-time occlusion and oblique-friendly instance segmentation framework, Keypoint-Mask, to solve the above problem. We empirically demonstrate the effectiveness of our framework on the MS COCO and industrial-level datasets. Our framework achieved 35 FPS and 37.6% on the COCO test-dev instance segmentation, outperforming similar lightweight models such as Blender-Mask, Center-Mask, and Yolact. Furthermore, Keypoint-Mask achieved the highest 71.7% on our collected case dataset, filling the gap that all the state-of-the-art models cannot reach. The dataset is labeled in the COCO format and made publicly available.
Upper Hessenberg decomposition matrix utilized to build zero watermarking technique in YCbCr space

The properties of matrices as a subject in linear algebra are used in the topic of image processing via employing many algebraic methods in several areas, including security. The digital image watermarking techniques represent the security technique type addressed in this work. This paper aims to implement and demonstrate the possibility of successfully using the algebraic Hessemberge decomposition method (HDM) for the first time in building a zero watermarking technique as a transform to extract the features of the image without using any of the customary transformations. Also, the improvement of the results of the technique is performed by applying discrete wavelet transform (DWT) as an additional transform to increase the accuracy in the extraction of the watermark. Two techniques are adopted to achieve the aim. The basic idea behind both techniques is to make use of the algebraic Hessemberge decomposition method (HDM) as a mathematical tool to transform the image into another domain. In the first one, the YCbCr is applied to the color image to obtain the Y component. The HDM is carried out to create one matrix from the Hi matrices to get the master-secret. In the second technique, the DWT is utilized on the Y component to obtain the low-frequency band (LL). which is a low-resolution approximation of the original image. After that, the HDM is used to create one matrix from all the Hi matrices to get the master-secret. The experimental results show that the NC values under a lot of the attacks are improved in the second technique. But the NC values of salt and pepper attack in the first technique are higher. Some attacks retained the same values of the NC in both techniques.
From Lab to Reality: Optimization of Industrial Augmented Reality Interfaces

My thesis work is focused on the creation of guidelines for the authoring of the next-generation Industrial Augmented Reality Technical Documentation. The research project aims to provide clear recommendations to optimize the template presentation related to the instruction authoring in an industrial context focusing attention in assessing the real economic impact that AR can offer for companies. This optimization not only will improve the operators' performance by providing them with more easily interpretable information, but also will reduce developers' computational costs to companies in both programming and 3D modeling. Finally, the outcomes obtained will provide guidelines that can make the authoring of AR technical documentation accessible even to technical writers who are not particularly skilled in AR.
Optical Crosstalk Mitigation for Individual Addressing in a Cryogenic Ion Trap

We present an experimental realization of coherent crosstalk cancellation in a trapped ion cryogenic system. The qubit register consists of a linear string of calcium ions which are individually addressed using tightly focused laser beams oriented perpendicular to the crystal axis. This individual control is provided by a multi-core photonic crystal fiber that allow for cancellation pulses to be applied to neighbouring ion sites to destructively interfere with crosstalk from addressing pulses on target qubits. With this technique we find > 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> fold reduction in intensity crosstalk.In order for this novel method of crosstalk elimination to be stable, phase coherence must be preserved between the cancellation and addressing beams. We further develop the appropriate calibration methods and procedures to mitigate phase drifts between these different optical paths. This includes a method to minimize the effects of variable duty cycles of individual pulses that may cause for differential phase noise.In addition, we extended this work by including the results a laser written waveguide array used to provide single-ion addressing capability, which is found to produce intensity crosstalk on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> . This device may be used in combination with the approach of optical cancellation developed for crosstalk compensation to result in negligible crosstalk errors between neighbouring qubit. As a consequence, such a system may allow for quantum error correction and fault-tolerant circuits to be viable as the system scales up to larger numbers of qubits.
HardGBM: A Framework for Accurate and Hardware-Efficient Gradient Boosting Machines

Gradient boosting machine (GBM) is a powerful and widely used type of ensemble machine learning methods, among which the most famous one is XGBoost (XGB). However, the cost of running large GBMs on hardware could become prohibitive given stringent resources. Ensemble reduction on boosting ensemble is intrinsically hard because member models are constructed in a sequential order, where the training targets for latter ones depend on the performance of the former ones. In this work, a GBM reduction framework is proposed for the first time to tackle the problem. For the first time, the framework supports automatic hardware implementation of regression tree ensembles. Experiments on 24 datasets from various applications demonstrate that our method reduces overall area utilization by 81.60% (80.64%) and power consumption by 21.15% (19.06%), while exceeding or successfully maintaining the performance level comparing with the original XGB (LightGBM) ensembles. In comparative experiments, to attain approximately the same accuracy level as our framework or XGB, deep learning-based solutions require > <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$52.7\times $ </tex-math></inline-formula> footprints, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.0\times $ </tex-math></inline-formula> power consumption, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula> training time. Equipped with tunable parameters, the framework is expected to seek a Pareto optimal front considering hardware resource limitation, accuracy and stability, and computation (training) efficiency.
Penerapan Metode Forward Chaining Pada Aplikasi Daring Untuk Mendeteksi Penyakit Anemia

An important factor in carrying out daily activities is the fulfillment of good health conditions. Many people do not care about the condition of the body so that it can affect health where in the end the disease will easily come to cause conditions that threaten the body to death. This indifference is due to being late in anticipating the situation so that it is often handled too late. Anemia is one of the diseases that often come unnoticed by the community where if it is handled too late, it will be life-threatening. Symptoms of anemia suffered by the community are usually treated by going to the puskesmas for examination. Examinations carried out by visiting the puskesmas certainly take time and energy where not all people have free time. Overcoming this, this research can help the community to find solutions quickly and effectively when health conditions occur and treat anemia symptoms. The experimental method in this type of qualitative research is the choice in the current research. Data analysis was conducted to collect data by interviewing sources of information related to the research material. The system development method applied uses the waterfall model where each stage of development is carried out gradually downwards. The system development is then implemented in the form of a web-based system using PHP programming and MySQL as the database. The technical language of the system development itself uses UML tools such as activity diagrams, use case diagrams, sequence diagrams. This research produces an application in the form of an expert information system to detect anemia based on references from experts using the Forfard Chaining Method. The Forward Cahining method is a forward reasoning method that uses facts as knowledge representations to draw conclusions. This developed system produces output in the form of a diagnosis calculation for the user whether he is suffering from anemia or not.
Application of the method of metaontologies in the intellectual analysis of text resources considering the fuzziness and blurring of images of natural language units

When forming an ontology, a heuristic approach to the formation and organization of knowledge is often used. The paper proposes a method of metaontologies in the intellectual analysis of text resources, taking into account the fuzziness and blurring of images of natural language units. The use of metaontology makes it possible to organize the search for the necessary data using various inference systems. In the process of information support, metaontology makes it possible to search for information across a large number of distributed heterogeneous data storages and to solve the problem of synonymy and ambiguity of object semantics. The mechanism of dynamic formation of syntagmatic patterns will reduce the impact of the quality of queries to the knowledge base on the quality of answers, as well as increase the speed of finding answers by training the question-answer system.
A Study on Upcoming Central Bank Digital Currency: Opportunities, Obstacles, and Potential FinTech Solutions using Cryptography in the Indian Scenario

The Indian government has decided to introduce a digital version of its currency, aptly named the 'Digital Rupee'. This announcement and the new dawn of the 5G mobile technology show India’s intent to lead the world in the Digital Revolution. This research paper seeks to study the various aspects of this announcement from an economic and financial perspective. The researchers have detailed the various possible benefits and risks of this decision based on the limited information provided by the government on the design of the central bank digital currency (CBDC). In particular, they have discussed the cybersecurity risks, such as hacking and government surveillance, and have suggested possible cryptography solutions like hashing and tokenization. The decision also means expanding the role of the Reserve Bank of India (RBI) from being just a central bank to providing payment services. The most important design characteristic of the Digital Rupee is its levy of interest. The paper discusses the cases of interest-bearing and non-interest-bearing Digital Rupee, along with their potential economic and monetary policy implications. The researchers have showcased various existing mathematical/ FinTech models for predicting the impact of CBDCs on the banking sector. They have commented that the short-term possibility of replacing cash with the Digital Rupee is non-existent due to the enormous volume of cash transactions in the Indian economy, whereas the long-term probability is much brighter. They have also compiled the country-specific challenges in implementing the CBDC, such as the population’s lack of financial literacy and the country’s weak technological infrastructure.
Implementasi Kontrol Proporsional Integral Derivative (PID) pada Robot Pengambil Gambar Untuk Keperluan Vlog

Video Blogs or Vlogs are a new way to present material interactively. During making videos, vloggers often find it challenging to carry out their activities in recording or taking videos themselves, so they need a camera operator. The presence of automation technology with a combination of computer vision can provide vloggers to produce work. The answer resulting from the variety of these two technologies is robot technology with a facial recognition system and can assist vlogger activities. Based on the test results, the automatic image-taking robot has face detection capabilities with a success rate of 80% for ten trials. This robot also can automatically maintain a distance from the vlogger, where the overall test results have a 90% success rate.
Marine Target Segmentation Based on improved DeepLabv3+

Marine target segmentation is of great significance to improve the automation of maritime management. In order to improve the accuracy of marine target image segmentation, a marine target image segmentation algorithm based on improved DeepLabv3+ network is proposed. Firstly, aiming at the problems of image distortion, blur and low contrast caused by weather and motion, Gaussian filtering, brightness enhancement, sharpening and histogram equalization are used to improve the image quality. Then the enhanced marine target image is brought into the improved DeepLabv3+ network for training. The features are extracted by the optimized improved Xception network, and then the multi -scale feature information is captured by the optimal dense prediction cell. In the decoder part, the further fusion of low-level features and high-level features improves the accuracy of the segmentation boundary and achieves the resolution of the original image. The experimental results show that this method can segment the marine target more accurately, and can basically meet the needs of the marine target segmentation task.
Hybrid Sampling Light Graph Collaborative Filtering for Social Recommendation

research-article Share on Hybrid Sampling Light Graph Collaborative Filtering for Social Recommendation Authors: Yefan Zhu School of Information and Management, Beijing Information Science & Technology University, China School of Information and Management, Beijing Information Science & Technology University, China 0000-0001-9044-2440View Profile , Li Zhang School of Information and Management, Beijing Information Science & Technology University, China School of Information and Management, Beijing Information Science & Technology University, China 0000-0003-4702-503XView Profile , Siqi Yang School of Information and Management, Beijing Information Science & Technology University, China School of Information and Management, Beijing Information Science & Technology University, China 0000-0002-0332-5690View Profile Authors Info & Claims CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software EngineeringOctober 2022Pages 121–128https://doi.org/10.1145/3569966.3570002Published:20 December 2022Publication History 0citation48DownloadsMetricsTotal Citations0Total Downloads48Last 12 Months48Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Blind JPEG Compression Artifacts Removal by Integrating Channel Regulation With Exit Strategy

Compression artifacts removal methods based on convolutional neural networks have attracted great attention. However, most existing methods require a specific trained model for a specific compression quality factor (QF), which inevitably leads to resource-consuming. Unfortunately, the QF is unknown in most practical applications, so it is intractable to choose a suitable model. In this work, we experimentally analyze the relationship between compression index estimation and compression artifacts removal. Based on the connection between them, we couple compression index estimation with compression artifacts removal into a unified network. A network named CRESNet is proposed, working for a wide range of QFs by integrating channel regulation with an exit strategy. Specifically, CRESNet adopts a multi-stage progressive structure with an exit strategy embedded to automatically select the optimal exit stage according to the estimated compression index reflecting the difficulty of the input sample. Benefiting from the exit strategy, CRESNet removes artifacts from slightly compressed images through a simple process while doing an elaborate process for severely compressed images. Furthermore, a compression-information-guided channel regulation (CICR) mechanism is developed to adaptively regulate feature maps based on the estimated compression index. CRESNet achieves a more elegant trade-off between artifacts removal and detail preservation in a resource-efficient manner. Experiments demonstrate that CRESNet achieves state-of-the-art performance.
Attribute-driven Interest Modeling for Sequential Recommendation

User interest modeling in sequential recommendation is to learn the underlying interests from the user's historical interaction records, and then predict a list of items that the user might interact with. Existing studies treat each item as an integral unit, and mainly focus on extracting useful information from the item sequence directly. However, they neglect the fact that a user usually interacts with an item because of some attributes of it, and the cumulative result of attributes reflects the user's more specific interests. In this work, we propose a novel approach named Attribute-driven Interest Modeling for sequential recommendation (AIM), and explore user latent interests in a fine-grained manner. More specifically, our attribute-driven module captures more accurate interests from the attribute sequence, where each attribute first aggregates information from its neighbor items with a graph convolutional network. Then, the learned multiple interests are aggregated with the base interest extracted from the original item sequence to form user representation. Finally, the user representation is used to calculate the ranking score and make predictions. Extensive experiments on three real-world datasets show that our model outperforms the state-of-the-art baseline by 0.49% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim$</tex> 8.61%.
A new robust watermarking algorithm based on intra-frame difference

Digital watermarking is an effective way to protect the copyright of digital medium. However, most existing video watermarking algorithms cannot resist both spatial and temporal attacks as they cannot utilize the temporal and spatial video redundancy simultaneously. In this paper, based on spatial domain intra-frame difference, a new robust video watermarking algorithm is proposed. Firstly, the frame sequences of the to-be-marked digital video are grouped based on the time period. Then, by a spatial domain image watermarking algorithm, all frames within each group are embedded with one-bit data, and the watermark is finally embedded. Extensive experiments are conducted, and these results verify that the proposed algorithm is more robust than some other video watermarking methods under the same impact on the video quality.
Embodied AI and Musicking Robotics

This chapter discusses a hypothesis for embodied AI and its development with music robots. It proposes a solution to the challenge: if we want AI/robots to join us inside the creative acts of music, then how do we design and develop systems that prioritise the relationships that bind musicians inside the flow of musicking? This requires significant thinking around some core questions such as ‘what does AI need to do in order to stimulate an embodied interaction in music?’, ‘what sort of intelligent agent does the AI need to be?’ and ‘what does the machine need to learn; what is to be modelled?’. I proffer a definition of embodied AI as an intelligent agent whose operational behaviour is determined by percepts (Percepts are objects of perception, or put another way, something that is perceived) interacting to the dynamic situation within which it operates, and outline foundational theories such as embodiment, embodied cognition, flow, musicking, meaning-making and embodied interaction to argue for such a concept in music. Following this, I outline the theoretical and conceptual structures that I have developed in order to get embodied AI and musicking robots to create music and meaning with human-musicians through which I offer examples of some of the works that have been developed using this approach.
Effects of Ease of Use “Nigirikawa” in Kyudo on Grip Force of “Tenouti”

Nigirikawa is a leather tape attached to Japanese bow in Kyudo. It assists the “Teuchi” (the method of gripping bow), which is to twist the bow counterclockwise to make the arrow fly straight. Kyudo players select the materials of the Nigirikawa for ease of use from subjective impression. The objective of this study is to examine the effectiveness of grip strength in drawing the bow as an objective evaluation index. We attached four pressure sensors to the grip of the bow to measure the pressure during shooting and compared the four types of Nigirikawa (deer, shark, cow, and sheep). The results showed that the differences of gripping force depending on the ease of use of the Nigirikawa in two movements of “Daisan” and “Kai”. Therefore, we concluded that gripping force is effective as an objective evaluation index of Nigirikawa.
Augmented Reality based 3D Business Card implementing Virtual buttons

In recent years, Augmented Reality (AR) has started becoming an omnipresent technology. This massive growth and spread will reshape the current world dynamics, especially the upcoming business niches and possibilities. AR has been continuously improving the real-world visuals through augmented content generated effortlessly by the people using this technology. The magnification in the number of smartphone users and ease of instant access to mobile devices has made it viable for everyone to utilize AR technology. This paper presents an innovative approach to promote the business and bring customer’s attention to its product and portfolio using augmented reality 3D business cards. Since this AR business card could inculcate information about the card owner in the form of audio, video, images, and animated moving objects, which are way beyond than an ordinary printed business card could ever incorporate. It appears as a regular business card at first glimpse, but it opens up into an interactive 3D augmented business card on scanning it through a mobile device. This AR business card comes with the benefit that the card could be customised anytime according to the changing business and market needs.