Reviewerly/exHarmony huggingface dataset:
Modeling and Analysis of Video Image Target Detection (TD) and Tracking Based on Deep Learning

In recent years, deep learning technology and some related neural network models have shown strong learning ability in hierarchical feature learning, and have made great achievements in image classification, target recognition and scene understanding. Considering the strong hierarchical feature learning ability of deep network model, a correlation filter detection and tracking model based on dynamic spatial feature selection is proposed in this paper; In order to further improve the mathematical modeling method of the algorithm and improve the accuracy of the algorithm, a correlation filter detection and tracking model based on the fusion of spatial feature selection and channel feature selection is proposed; In order to optimize the mathematical modeling method of the algorithm and improve the real-time performance of the algorithm, an accelerated correlation filter detection and tracking model based on improved alternating direction multiplier method is proposed. The experimental results show that the depth feature of the algorithm proposed in this paper has excellent tracking accuracy and robustness, can be well applied to real-time tracking scenes, realizes model dimensionality reduction and improves the detection and tracking performance; In otb100 dataset, this algorithm achieves the optimal position prediction success rate under the 20 pixel error threshold and the tracking success rate under the 50% overlap threshold. The success rate curve srdcf is slightly higher than that of AUC algorithm; In templecolor128 dataset, although the algorithm proposed in this chapter only obtains the second overlapping accuracy and the third position accuracy, its tracking accuracy is very close to that of stable and srdcf algorithms; In terms of the real-time performance of the algorithm, the algorithm proposed in this paper can well meet the needs of real-time tracking.
Location Aware Super-Resolution for Satellite Data Fusion

Satellite data fusion involves images with different spatial, temporal, and spectral resolution. These images are taken under different illumination conditions, with different sensors and atmospheric noise. We use classic super-resolution algorithms to synthesize commercial satellite images (Pléiades) from a public satellite source (Sentinel-2). Each super-resolution method is then further improved by adaptive sharpening to the location by use of matrix completion (regression with missing pixels). Finally, we consider ensemble systems and a residual channel attention dual network with stochastic dropout. The resulting systems are visibly less blurry with higher fidelity and yield improved performance.
Regret-Index Neural Network for Finding the Maximum Data Flow of Self-Powered Wireless Sensor Networks

There are many complex situations in self-powered wireless sensor networks. First, the signal fluctuation will cause the upper limit of the data transmission volume between the sensor and the base station to change at different times. In addition, with long-term use, equipment failure will cause the loss of some data during transmission, and the rate of data loss is usually undetermined. Based on the above complexity, to determine the maximum data flow of self-powered wireless sensor networks, the fuzzy-wastage time-varying maximum flow problem (FTMFP) is introduced in this article. Existing algorithms are not suitable for solving the FTMFP due to the uncertainty and time variation in the problem. To solve this problem, a regret-index neural network (RINN) algorithm is proposed in this article. The unique minimum regret-index path selection (MRPS) mechanism of the RINN algorithm improves the accuracy when obtaining solutions. The parallel computing and training-free mode of the RINN algorithm improve the computational speed. The Utah channel impulse response (CIR) wireless sensor network and the large-scale New York City road monitoring network are employed to evaluate the performance of the proposed RINN algorithm. The time variance of the data transmission caused by signal fluctuations and the fuzzy loss ratio of data caused by equipment failures are considered in the network. Compared with the well-investigated Ford–Fulkerson (FF), Edmonds–Karp (EK), and Cai–Sha (CS) algorithms, experimental results have proved the effectiveness of the RINN.
Comparison of Rabin-P And Rabin Takagi Encryption Schemes on IoT Platform

Encryption is the process of encoding data in such a way that it is unreadable to anybody who does not have access to it. Only authorized persons with a "key" may read or use data that has been encrypted. The encryption technologies are classified into two which are symmetric-key encryption and asymmetric encryption. Symmetric encryption is a typical approach in which the encryption and decryption keys are the same. Asymmetric encryption is a technique that creates two keys, one private and one public. These keys are not similar, and the public key may be shared with everyone, whilst the private key is only shared with those who are authorized to view the data. Users can encrypt the data using the public key, and the decryption key is the private key. The Rabin-p encryption algorithm is one of the public key encryptions. The Rabin-p's runtime and performance on a microprocessor architecture were investigated in this study. As a result, the Raspberry Pi is employed in this project with the Raspbian OS, a smaller version of the Linux operating system for embedded devices used. The Rabin-p and Rabin-Takagi cryptosystems are compared on the Raspberry Pi setup. It is possible to infer that Rabin-p outperforms the Rabin-Takagi cryptosystem on the microprocessor platform. Rabin-p improved the decryption performance and decryption failure by producing only one modular exponentiation and generating a unique message at the end of the decryption process. The runtimes to complete both types of Rabin variation have been documented. Rabin-p is faster than Rabin-Takagi for encrypting and decrypting data, according to the findings obtained.
Prospects of pose estimation as an input in gaming

Although there have been attempts to use Gesture-Based User Interaction in interfaces, not just in games, they require some special hardware. For example, Xbox’s Kindest controller. Although it is very good at what it does, the cost and its proprietary nature restrict its use drastically. With this research, we aim to bridge the experience of already existing expensive and proprietary technologies with the cost-effectiveness of cheap accessories like basic webcams and nominal processing for image processing and gesture recognition. A pose estimation-based gaming interaction system would require a media capturing device, a processing unit, and a display device to show the output. We expect to use the inbuilt webcam on our laptops as the capturing media device. Our dedicated GPU or integrated GPUs have become powerful enough to sustain loads to perform pose estimation and gesture recognition while running a game in parallel. We get in-depth and look at the different approaches that are present in pose estimation. There is 2D Pose Estimation, and there is 3D pose estimation. We carry out intense research in implementing these two techniques and implementing them in a Unity game environment on a Game Object and making it interactive with the environment better.
Diversity in Stakeholder Groups in Generative Co-design for Digital Health: Assembly Procedure and Preliminary Assessment

Diverse knowledge and ways of thinking are claimed to be important when involving stakeholders such as patients, care professionals, and care managers in a generative co-design (GCD) process. However, this claim is rather general and has not been operationalized; therefore, the influence of various stakeholders on the GCD process has not been empirically tested.In this study, we aimed to take the first step in assessing stakeholder diversity by formulating a procedure to assemble a group of diverse stakeholders and test its influence in a GCD process.To test the procedure and assess its influence on the GCD process, a case was selected involving a foundation that planned to develop a serious game to help people with cancer return to work. The procedure for assembling a stakeholder group involves snowball sampling and individual interviews, leading to the formation of 2 groups of stakeholders. Thirteen people were identified through snowball sampling, and they were briefly interviewed to assess their knowledge, inference experience, and communication skills. Two diverse stakeholder groups were formed, with one more potent than the other. The influence of both stakeholder groups on the GCD process was qualitatively assessed by comparing the knowledge output and related knowledge processing in 2 identical GCD workshops.Our hypothesis on diverse stakeholders was confirmed, although it also appeared that merely assessing the professional background of stakeholders was not sufficient to reach the full potential of the GCD process. The more potently diverse group had a stronger influence on knowledge output and knowledge processing, resulting in a more comprehensive problem definition and more precisely described solutions. In the less potently diverse group, none of the stakeholders had experience with abduction-2 inferencing, and this did not emerge in the GCD process, suggesting that at least one stakeholder should have previous abduction-2 experience.A procedure to assemble a stakeholder group with specific criteria to assess the diversity of knowledge, ways of thinking, and communication can improve the potential of the GCD process and the resulting digital health.
An Improved Transformer Model Based Graph Node Classification Method

In recent years, Transformer models have been widely developed in various fields, such as natural language processing (NLP) and computer vision (CV). However, its application in graph data structures is still limited. It is crucial to develop an improved Transformer model for graph data. Considering the weakness that the original Graph Transformer (GT) is difficult to capture location information of nodes significantly, this paper combines node positional embedding in each layer's output. Then, in order to select more efficient neighbor nodes, a mask matrix is introduced to reconstruct attention score matrix. Finally, the group normalization is applied to provide faster training speed and better generalization performance. A number of experiments on two reference graph datasets demonstrate that the proposed GT model has better performance compared to other standard GNN s and original G T.
An Energy-Efficient YOLO Accelerator Optimizing Filter Switching Activity

Convolutional neural network (CNN) based object detectors such as the you-only-look-once (YOLO) achieve remarkable performance but come with high computing complexity and a large memory bandwidth. Therefore, it is challenging to design an accelerator for such object detectors on edge devices, which have a limited power budget and relatively small on-chip memory footprint. In this paper, we propose an energy-and memory-efficient CNN accelerator for YOLO. First, we propose a novel dataflow which reduces the amount of filter switching by 99.56% on average. Second, we propose a layer-wise on-chip memory reuse scheme in which multi-bank on-chip buffers are efficiently utilized for both feature maps (FMs) and filters without the need to access external memory for FMs. The proposed design is implemented on a Xilinx ZC706 FPGA and consumes power of 5. 05W but achieves a throughput rate of 370.5 GOPS for Tiny-YOLOv2 while using only 640 DSPs and 322.5 BRAMs. Our design achieves energy efficiency of 73.39 GOPS/W, thus outperforming those in previous works.
Classification of compromised DOFS data with LSTM neural networks

Distributed optical fiber sensors (DOFS) are gaining momentum for in-situ condition monitoring and damage detection purposes.Although DOFS are a versatile sensing method enabling high-resolution strain and temperature mapping, they are also sensitive to mechanical vibrations.Vibrations are typically created by the ambient environment (e.g acoustic background, rotating equipment) which can produce high levels of measurement noise [1].With physical access to DOFS installations, the principle of acoustic or mechanical vibrations can also be utilized for malicious sensor tampering.The current lack of anomaly-detection systems suggests that practical DOFS applications would benefit from an automated analysis to detect and classify compromised measurements.Noise classification makes it possible to identify its source and potentially remove its effects from the measurement in the future.This would expand the commercial applications of DOFS systems significantly.Neural networks have been used for error detection in cyber-physical applications in numerous studies with high-accuracy results [2].Specifically, long short-term memory (LSTM) neural network models have become popular in recent years to classify anomalies in sequential e.g time-series data.Our investigation conducted a series of physical experiments using magnitude-controlled mechanical disturbances on bare free-hanging DOFS.Both random low-frequency vibrations at large displacement amplitudes and a constant high-frequency acoustic source at a low amplitude were employed.Experiments revealed that strain patterns are visually different with varying types and levels of disturbances.For the numerical analysis, statistics and machine learning-based approaches were applied for DOFS vibration noise classification, and their accuracy is discussed in detail.Results from the post-processing of compromised DOFS data suggest that it is possible to develop a vibration detection or classification system based on off-the-shelf DOFS interrogation equipment coupled with LSTM numerical tools.
Shakespeer: Verifying the Co-presence of Smart Devices and Users via Vibration

Securely and unobtrusively authenticating a user is an important problem given the pervasiveness of smartphones. Existing approaches, such as password, fingerprints, or facial recognition, are vulnerable to various attacks, and/or degrade usability. To overcome this problem, we propose Shakespeer, which differentiates users based on uniqueness in the propagation of haptic vibrations through hand, forearm muscles and bones. These vibrations are generated by the user’s smartphone and sensed by their smartphone and smartwatch. The unobtrusive haptic vibrational response makes this biometric feature hard to be replicated. Meanwhile, it provides the co-presence detection function, which allows the devices to confirm the co-presence on the user’s body. We implement Shakespeer using smartphones and smartwatches and tested it across 32 subjects under real-world settings. From our preliminary exploratory evaluation, Shakespeer achieves an equal error rate (EER) of 0.59 %, demonstrating its feasibility.
Theory of the optimum signal approximation clarifying the importance in the recognition of parallel world and application to secure signal communication with feedback

In this paper, it is shown a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the ma-trix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detail algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{E}(\omega)=\mathbf{F}(\omega)-\mathbf{Y}(\omega)$</tex> between the matrix operator-input-signals <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F}(\omega)$</tex> and the matrix operator-output-signals <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Y}(\omega)$</tex> of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback do not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic con-sideration of the recognition of human. Based on this discussion, it is shown naturally, why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important create a parallel world of conception where we share the set of invisible error signals including the words and the consciousness of both worlds.
The Engineering Implications of Code Maintenance in Practice

Allowing developers to move fast when evolving and maintaining low-latency, large-scale distributed systems is a challenging problem due to i) sheer system complexity and scale, ii) degrading code quality, and iii) difficulty of performing reliable rapid change management while the system is in production. Addressing these problems has many benefits to increase system developer efficiency, reliability, performance, as well as code maintenance. In this paper, we present a real-world case study of an architectural refactoring project within an industrial setting. The system in scope is our codenamed ItemIndexer delivery system (I2DS), which is responsible for processing and delivering a large number of items at rapid speed to billions of users in real time. I2DS is running in production, refactored live over a period of 9 months, and assessed through impact validation studies that show a 42% improvement in developer efficiency, 87% improvement in reliability, 20% increase in item scoring, a 10% increase in item matching, and 14% CPU savings.
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning

Existing video frame interpolation methods can only interpolate the frame at a given intermediate time-step, e.g. 1/2. In this paper, we aim to explore a more generalized kind of video frame interpolation, that at an arbitrary time-step. To this end, we consider processing different time-steps with adaptively generated convolutional kernels in a unified way with the help of meta-learning. Specifically, we develop a dual meta-learned frame interpolation framework to synthesize intermediate frames with the guidance of context information and optical flow as well as taking the time-step as side information. First, a content-aware meta-learned flow refinement module is built to improve the accuracy of the optical flow estimation based on the down-sampled version of the input frames. Second, with the refined optical flow and the time-step as the input, a motion-aware meta-learned frame interpolation module generates the convolutional kernels for every pixel used in the convolution operations on the feature map of the coarse warped version of the input frames to generate the predicted frame. Extensive qualitative and quantitative evaluations, as well as ablation studies, demonstrate that, via introducing meta-learning in our framework in such a well-designed way, our method not only achieves superior performance to state-of-the-art frame interpolation approaches but also owns an extended capacity to support the interpolation at an arbitrary time-step.
Estimating the Impact of Communication Schemes for Distributed Graph Processing

Extreme scale graph analytics is imperative for several real-world Big Data applications with the underlying graph structure containing millions or billions of vertices and edges. Since such huge graphs cannot fit into the memory of a single computer, distributed processing of the graph is required. Several frameworks have been developed for performing graph processing on distributed systems. The frameworks focus primarily on choosing the right computation model and the partitioning scheme under the assumption that such design choices will automatically reduce the communication overheads. For any computational model and partitioning scheme, communication schemes — the data to be communicated and the virtual interconnection network among the nodes — have significant impact on the performance. To analyze this impact, in this work, we identify widely used communication schemes and estimate their performance. Analyzing the trade-offs between the number of compute nodes and communication costs of various schemes on a distributed platform by brute force experimentation can be prohibitively expensive. Thus, our performance estimation models provide an economic way to perform the analyses given the partitions and the communication scheme as input. We validate our model on a local HPC cluster as well as the cloud hosted NSF Chameleon cluster. Using our estimates as well as the actual measurements, we compare the communication schemes and provide conditions under which one scheme should be preferred over the others.
Memory Efficient Video Collection and Extraction Technique for Augmented Triplet Network's Dataset

Wild Deer grazing in farmers' and villagers' fields has caused serious trouble for the residents and concerned authorities in Northern Hokkaido of Japan. Furthermore, the number of deer in northern Japan is increasing rapidly leading to increment in road accidents. Deer-car collisions each year kill millions of people around the world which is one of the serious challenges that needs to be handled through scientific study. Several countermeasures have been applied to avoid or minimize to reduce deer-car crashes and the damages caused by the white-tailed deer, but a solid solution is yet to be found. In this context, we proposed a highly effective solution to identify the individual wild deer and pinpoint the most troubles causing the group of deer using Augmented Triplet Network (ATN), a novel technique of computer vision. Initially, the deer are recognized and classified individually which will help us track the deer and know the behavior of the deer in depth. The aim of this study is to reduce the video data by automatically extracting only the data necessary to construct a dataset for individual identification in the ATN model. The extraction method proposed in this study reduced the size of videos by 81 percent on average. We were able to obtain an accuracy rate of over 99 percent on two different datasets by using the ATN model.
Supervised Disentangled Graph Learning for OCM

In Chap. 4, we studied the fine-grained outfit compatibility modeling, where the hidden factors affecting the outfit compatibility are jointly considered. One key limitation is that it only investigates the visual content of fashion items while overlooking the items’ semantic attributes. The item attribute labels usually contain rich information that characterizes the key item parts, which can be adopted to supervise the attribute-level representation learning, and hence promote the model’s performance as well as interpretability. Thus, in this chapter, we aim to fulfill the fine-grained outfit compatibility modeling by incorporating the semantic attributes of fashion items.