Reviewerly/exHarmony huggingface dataset:
Kinematics analysis and simulation of a cooperative manipulator

A cooperative manipulator was designed. The kinematics model was established by using the Modified-DH method, and the kinematics equation was completed. The Monte Carlo method was used to solve the robotic arm workspace, and the point cloud diagram of the robotic arm end effectors workspace was drawn. The trajectory planning simulation study of the robotic arm was carried out through Matlab, and the displacement, velocity, and acceleration change curves of each joint during the movement were drawn. The rationality of the manipulator structure design was verified, and providing guidance for the subsequent research work of the manipulator.
Search for identical regions in images using invariant moments

One of the ways to describe objects on images is to identify some of their characteristic points or points of attention. Areas of neighborhoods of attention points are described by descriptors (lots of signs) in such way that they can be identified and compared. These signs are used to search for identical points in other images. The article investigates and establishes the possibility of searching for arbitrary local image regions by descriptors constructed with using invariant moments. A feature of the proposed method is that the calculation of the invariant moments of local areas is carried out with using the integral representation of the geometric moments of the image. Integral representation is a matrix with the same size as the image. The elements of the matrix is the sums of the geometric moments of individual pixels, which are located above and to the left with respect to the coordinates of this element. The number of matrices depends on the order of the geometric moments. For moments up to the second order (inclusively), there will be six such matrices. Calculation of one of six geometric moments of an arbitrary rectangular area of the image comes down up to 3 operations such as summation or subtraction of elements of the corresponding matrix located in the corners of this area. The invariant moments are calculated on base of six geometric moments. The search is performed by scanning the image coordinate grid with a window of a given size. In this case, the invariant moments and additional parameters are calculated and compared with similar parameters of the neighborhoods of the reference point of different size (taking into account the possible change in the image scale). The best option is selected according to a given condition. Almost all mass operations of the procedures for calculating the parameters of standards and searching of identical points make it possible explicitly perform parallel computations in the SIMD mode. As a result, the integral representation of geometric moments and the possibility of using parallel computations at all stages will significantly speed up the calculations and allow you to get good indicators of the search efficiency for identical points and the speed of work
Hybrid ResNet: A Shallow Deep Learning Architecture for Moderate Datasets

Deep learning models usually require large memory space and computation power for training on vast size datasets. This costly overhead creates a bottleneck in the research and development of neural network models. Efficient and resourceful deep learning models that work on the balance between computation cost and performance are required. Given these scenarios of less performance improvement over exponential computational cost increase, we have implemented two shallow neural network architectures based on ResNet. One is the naive version of the ResNet model; keeping its main design features and configuration. The second model uses a combination of residual and non-residual blocks primarily for effective feature learning and computation efficiency. We compare and evaluate these architectures performance in a rather simple environment. We found that simple Resnet in less number of layers is computationally expensive for small datasets. Performance improvement is observed with our custom architecture of ResNet. This new approach can be implemented for larger-scale neural networks for achieving computation efficiency.
A Tool to Implement Adaptive Audio Techniques on Unity Games

Developing an effective and simple audio system that can adapt to in-game events can take time and become complex, specially to beginner developers that don't know how to use adaptive audio techniques. To make this less challenging, we developed a tool to implement adaptive audio into Unity games. The Adaptive Audio Manager tool for Unity is an accessible package that provides classes and methods which help developers implement a functional audio system in their games, using two popular adaptive audio techniques: vertical layering and horizontal re-sequencing. Along with the framework code, we provide documentation and a Unity prototype project with examples to keep the learning process on how to use this framework simple.
Correlation Filter Tracking via Global and Adaptive Local Parts

Owing to the excellent performance and high efficiency, correlation filters have attracted attention in visual tracking recently. However, they often lead to tracking failures because of the high sensitivity to occlusion. Part-based tracking methods can deal with partial occlusions to some extent, but the ability of these methods to cope with other challenges, such as scale variations, or low resolution, is not robust enough. In this work, we propose a novel correlation filter-based tracking approach via global and adaptive local parts to address this issue. Specifically, the object is divided adaptively according to its appearance at first. For local parts tracking, coarse position and scale factor are then obtained by employing the spatial-temporal information of reliable parts. Finally, global tracking is performed with the rough position to localize the object more accurately. Experiments illustrate that the proposed tracker outperforms several state-of-the-art methods.
An Early Detection of Breast Cancer Using Hybrid Ensemble Classifier

In the past few years, India has reported 30% of breast cancer cases, and this number is likely to increase. In India, a woman is diagnosed with breast cancer every two minutes and dies every nine minutes. Women who are diagnosed and treated early can have a better chance for survival. This article offers a new machine learning-based strategy for diagnosing breast cancer known as an Enhanced ensembled classification model. Further, this research work has conducted an experimental analysis to check the validity of the dataset extracted from the Kaggle repository. When compared to other algorithms such as Logistic Regression and SVM, the proposed model provides more accurate and effective outcomes when implemented and compared with existing methods.
Research on the recognition and location method of stacked bagged agricultural materials based on monocular vision

In this paper, we propose a monocular vision based recognition and location method to solve the problom of recognition and location of stacked bagged agricultural materials in the train, including image recognition algorithm based on watershed transformation and target location algorithm based on image moments. In addition, the proposed algorithms are validated by carrying out experiments on the machine vision test platform for 25kg flour bags. The experimental results showed that the image recognition algorithm based on watershed transform is good. The target location algorithm based on image moments can effectively control the camera to move to the desired position.
Clustering Lakes in Kampar by Using Morphometry Data and Machine Learning Analysis

Abstract Lakes have an important role in hydrological and biochemical cycle. It also has some other crucial role such as domestic and industrial water use as well as irigation. The monitoring and management of this aquatic resources is crucial. But with many numbers of lakes, it is very challenging to manage them all. Clustering lakes can provide the answer so the management of the same cluster lakes may be done efficiently. Within this study, morphometry data of 6 lakes in Kampar Regency, Riau Province, were analyzed by using one of artificial intelligence branch which is machine learning. Morphometrical data are collected by using information geographic system. These data then categorized by using python language. This categorization based on data mining categorization algorithm named K-means. Based on the K-means machine learning clustering, the optimum cluster based on Elbow methid is k=3. But there is a possibility to look around for k=2. Based on K=3, cluster 3 is defined as the lowest values of all atribute. Based on k=2, the lowest value of morphometry data wiil be in the cluster 1.. These data will not only provide basic data such as total area, shape, width and length, but also help to understand the large scale hydrological models.
Practical study on the semantic analysis algorithm based on text classification

With the development of information technology, the Internet data and resources have been greatly increased, showing the characteristics of massive amounts. In order to effectively manage and utilize these massive distributed information, content-based information retrieval and data mining have gradually become a field of attention in recent years. Among them, text classification (text categorization, TC) technology is an important basis for information retrieval and text mining. Its main task is to train the classified models based on the documents of a given category and the content of the documents, and then to judge or predict the categories of new documents through the classified models. This paper discusses the characteristics of the social network and the problems, in the future development of semantic analysis algorithms on text classification will also become more obvious.
Image enhancement method based on perceptual excitation mapping and gradient attenuation

Aiming at the problem of unclear observation of low-illuminance images under complex exposure conditions, this paper transforms the image to be processed into the brightness space, and uses the principle of perceptual excitation model to design a brightness mapping function to adjust the dynamic range of image pixels. At the same time, the bias power function is introduced, and the gradient image of the image is obtained through the Poisson equation. Through the adaptive gradient attenuation method, the brightness step phenomenon of the image is suppressed in the gradient domain, and the severe color fluctuation of the exposure edge is effectively controlled, and the image is strengthened. The ability of detail representation and the harmony of the overall color. Experimental results show that this method can effectively enhance low-illuminance images, avoid the interference of high-exposure sources on the global image channel, retain high details, and colors are naturally coordinated, and the overall visual effect of the processed image is good.
On Improving Convolutional Networks Based People Detection with Fisheye Cameras

In this paper, we propose a new method to train convolutional neural networks for detecting people in images taken with ceiling-mounted fisheye cameras. While simply fine-tune existing detectors using annotated images lead to increased false positives due to lack of variety in the training data, we find that adding automatically computed backgrounds of the target scene in the training process yields much better detection accuracies. This allows us to build practical scene-specific human detectors.
Fostering communication and collaboration through regenerative design games.

This paper explores how regenerative design games might foster the communication and collaboration skills needed to scaffold transitions to just and sustainable futures.It explores three design games, each co-designed with different people, for different intents and purposes but each with the shared aim to build people's capacity for regeneration, open communication and effective collaboration.It discusses the role of games in explorations of uncertain futures and highlights the important role played by care practices in co-design processes.
ADD: A Fine-grained Dynamic Inference Architecture for Semantic Image Segmentation

Dynamic inference that adaptively skips parts of model execution based on the complexity of input data can effectively reduce the computation cost of deep learning models during the inference. However, current architectures for dynamic inference only consider the exits at the block level, whose results may not be suitable for different applications. In this paper, we present the Auto-Dynamic-DeepLab (ADD), a network architecture that enables the fine-grained dynamic inference for semantic image segmentation. To allow the exit points in the cell level, ADD utilizes Neural Architecture Search (NAS), supported by the framework of Auto-DeepLab, to seek the optimal network structure. In addition, ADD replaces the cells in Auto-DeepLab with the densely connected cells to ease the interference among multiple classifiers and employs the earlier decision maker (EDM) to further improve the performance. Experimental results show that ADD can achieve similar accuracy as Auto-DeepLab in terms of mIoU with a 1.6 times speedup. For the fast mode, ADD can achieve 2.15 times speedup with only a 1.3% accuracy drop compared to those of Auto-DeepLab.
A Novel Clustering Scheme based on Density Peaks and Spectral Analysis

Clustering by fast search and find of density peaks (DPC) is a highly innovative clustering method published on Science in June 2014. The DPC algorithm assumes that the cluster center of each class is far away from each other and has a higher local density. However, this method has certain disadvantages: it needs to find the clustering centers of the data set, whereas finding the clustering centers for a data set with a complex structure is error-prone. Therefore, this paper proposes an algorithm (DPC-SC) that combines DPC and spectral clustering (SC). The algorithm first uses the DPC algorithm to pre-cluster the data set, extracting the core points of the data, and then exploits the spectral clustering algorithm to analyze the pre-clusterred data and to perform subsequent clustering. This method avoids the shortcomings of DPC in selecting complex data clustering centers, and also improves the clustering speed of spectral clustering significantly.Experimental evaluations show that DPC-SC is very competetive compared with several classic clustering algorithms.
Proxy-3S

Virtualization plays a key role in the area of Mobile Cloud Computing (MCC). In MCC, the protection of distributed VMs and mobile users’ sensitive data, in terms of security and privacy, is highly required. This paper presents a novel cloud proxy known as Three Policies Secure Cloud Proxy (Proxy-3S) that combines three security policies: VM users’ access control, VMs’ secure allocation and VMs’ secure communication. The proposed approach aims to keep the distributed VMs safe in different servers on the cloud. It enhances the access authorization to permit intensive distributed application tasks on the cloud or mobile devices while processing and communicating private information between VMs. Furthermore, an algorithm that enables secure communication among distributed VMs and protection of sensitive data in VMs on the cloud is proposed. Several experiments were conducted using a real-world healthcare distributed application. The experiments achieved promising results for high-level data protection and good efficiency rating compared to existing works.
Acceptability Studies in Semitic Languages

Formal acceptability experiments are particularly important for languages with smaller communities of linguists, including most Semitic languages. However, experimental studies of sentence acceptability in this language family are still rare, and focus on very few phenomena (notably wh-dependencies) and languages (mostly Arabic and Hebrew). This chapter reviews the extant literature on acceptability studies in Semitic languages. Special attention is given to studies on island constraints, resumptive pronouns – a conspicuous feature of Semitic –and their interaction, revealing a complex pattern of results. The scant literature on agreement and ditransitive structures is also discussed. The review also highlights some unexplored topics in Semitic syntax, which will benefit from future experimental work, including free word order, verbless sentences, and construct states. Finally, the chapter outlines some of the challenges facing researchers conducting experimental work in Semitic, including issues of diglossia, as well as technical challenges relating to script, online databases, and participant recruitment.
Material Composition of the Mesozoic Alkaline Rocks of the Yukhta Massif (Southern Yakutia, Central-Aldan Ore Region)

Abstract The paper considers the petrographic composition of the Mesozoic alkaline igneous rocks of the Yukhta massif. It is part of the Central Aldan ore region and is spatially located in the central part of the Nymnyr block. The massif is a large multiphase structure, of the most productive stage of the territory’s development – the Mesozoic tectonic-magmatic activation of the Aldan-Stanovoy shield. Determination of the qualitative quantitative-mineralogical characteristics of the 2 and 3 phases of intrusion (emplacement) most promising for gold-radioactive mineralization with the help of crystal-optical methods was the main goal of this work. As a result of petrographic studies of Mesozoic alkaline igneous rocks, it is defined that, the Yukhta massif is a multiphase magmatic structure, with decrease of the content of dark-colored minerals in rocks from the early to later phases of intrusion. In general, the rocks of the massif bear significant traces of secondary changes, which are related to the gradual formation of the massif. According to the features of the composition of the massif rocks, it was found that the latter could be formed from residual differentiates during the fractional crystallization of rock-forming minerals with the involvement of plagioclases. The Yukhta massif is associated with the large Samolazovskoye gold deposit, which formation is related to an intense contact-metasomatic impact on carbonate rocks. Hydrothermal-metasomatic transformations of the latter are the products of multi-stage silica-alkaline metasomatosis associated with the second and especially with the third phases of the massif intrusion, and with further weathering processes, involving karst formation, disintegration of gold-ore metasomatites and the formation of a thick oxidation zone. From whence it is concluded that uranium being a chemically active element does not accumulate in a hypergenic form within the Yukhta massif. Where the weathering crust is intensively developed, a gold-ore type of mineralization is observed. On the other hand, the rocks of the massif itself, in particular syenites of the 2 and 3 phases of intrusion, may be promising for the uranium-thorium-rare earth (U-Tn-REE) type of mineralization. In general, these studies in this direction will provide insight into a number of issues related to the study of the evolution and metallogeny of the Mesozoic tectonic-magmatic activation of the Aldan-Stanovoy Shield
Simulation Study on Flight Conflict Detection and Resolution in On-Sea Search and Rescue

In order to solve the problem of multi aircraft flight conflict in maritime search and rescue mission. This paper classifies the aircrafts performing maritime search and rescue tasks, defines the priority strategy of each aircraft, and carries out simulation experiments on five aircrafts in the mission scenario of multi aircraft maritime joint search and rescue. According to the predicted flight trajectory, the potential flight conflict points are obtained, and the aircraft release strategy in the case of multi aircraft conflict is obtained by combining the aircraft release priority rules, Generate conflict free flight path. Simulation results show that the proposed aircraft task classification method and priority strategy can implement the optimal path planning in time and avoid the potential flight conflict in the multi aircraft search and rescue mission.
Privacy-Preserving and Efficient Range Counting in IoT Networks

In recent years, Internet of Things (IoT) networks have been received much attention due to their intelligence and automation. Numerous devices are deployed in the networks, and many applications are willing to obtain the distribution of IoT devices in one range for data analysis. However, some distributions of IoT devices include sensitive information which may disclose the privacy of their owners. And most IoT devices cannot afford heavy computation because of their low computing power and limited storage capacity. In this paper, we propose a privacy-preserving and efficient scheme that satisfies local differential privacy and supports O(1) -time-complexity range counting query which can find the number of IoT devices in a range. It also allows IoT devices to join or exit dynamically. Finally, theoretical analysis proves that our scheme satisfies local differential privacy, and our experiments show that our range counting query scheme can get the microsecond level.
Futures in Things: Locating the Promise of Infrastructures in Public Libraries

Public libraries in the U.S. and around the world are rapidly changing due expanding technological and social needs of their communities. The Covid‐19 pandemic has intensified the debates about the future of public spaces and public services. In this paper, we report on a qualitative study of librarians in a U.S. urban public library system. The focus of the study was to understand how the concept of “the future of library” is constructed and contested both socially and materially. Using mixed methods, including participant observation, interviews, participatory design and action research, we developed insights about the socio‐political dynamics of futures in a public infrastructure. We argue that futures can be shaped not only by socio‐technical imaginaries, and representations, which tend to be abstract and distant, but also by socio‐material conditions in the present. Specifically, drawing on the work of infrastructure studies, we show how specific objects of librarians can be used to construct and impose a particular version of the future with which librarians have wrestle and to which they have to respond. In this paper, we focus on three examples in the public library: the circulation desk, the bookshelf, and the self‐check machine to show how they instantiate narratives and expectations about the future of libraries. During our study, these objects were redesigned as part of ongoing library renovations. The traditional circulation desk was changed to a small pod, thereby changing the embodied experience of librarians and setting up new kinds of interactions and expectations between librarians, the patrons and library space. The traditional stacks were replaced by smaller movable bookshelves making the space more configurable and more adaptable to future needs of the library. The new self‐check machine ascribed a set of values to the labor of librarians and introduced new management practices. Based on this argument, that the futures are socio‐material and infrastructural, we propose strategies for researchers who study the future and contribute to the growing body of research in anticipatory ethnography.