Reviewerly/exHarmony huggingface dataset:
An Improved Method of Dynamic Path Modification for Nonholonomic Mobile Robot

The TEB (timed elastic band) algorithm optimizes the path generated by the global planner to obtain a time-optimal trajectory that can avoid obstacles and satisfy the kinematics and dynamics constraints of the robots. In TEB algorithm, backward is often used to adjust the direction, which makes the trajectory not smooth enough. For differential driving robot, it is more convenient to rotate in situ to adjust direction. Excessive rates of change in acceleration can cause the torque output of the robot chassis motor to abruptly change, subjecting the robot to shock oscillation. In the improved TEB algorithm, the velocity constraint is modified and the specific jerk constraint is constructed. The improved TEB algorithm is simulated on the Stage simulation platform and implemented on a real differential driving robot. The experimental results show that the improved TEB algorithm can generate smoother forward trajectory without backward for wheeled differential driving robot, and the trajectory has good obstacle avoidance effect. Meanwhile, the algorithm can better solve the shock oscillation problem of the robot.
Reinforcing Intrinsic Walking Power with Nudge Reflecting Day-of-Week Pattern

In order to reduce the risk of lifestyle-related diseases, continuous exercise is necessary. However, many people are too busy to exercise on a consistent basis. Many existing approaches do not take into account the individual's environment and are one-sided in their support. Therefore, they fail to motivate individuals to exercise essentially. In this study, we propose an intervention that takes the environment into account and is tailored to the individual. The proposed method takes into account the busyness of the subject based on the periodicity of their walking volume, and nudge-based messages are sent to them. The use of periodicity and nudges enables consideration of the individual's environment and motivation for intrinsic exercise and is expected to lead to sustained exercise.
Research and Analysis for the Network Improvement with Blockchain and Historical Nihilism

As the rapid development of network and Blockchain technology, there are a lot of challenges associated with Blockchain and network based applications. In this paper, targeted at the security issues of these applications, especially combined with historical nihilism influence, a comprehensive researches and analysis has been made, where the new characteristics of historical nihilism is presented in the latest Blockchain network environments. And then the significant influences of historical nihilism imposed on network environments are analyzed, especially for the impact on the identification for different human groups with politics ideology. In addition, the challenges of preventing historical nihilism under new media scenarios are also analyzed. Based on these new characteristics and challenges of historical nihilism presented in Blockchain network, several efficient solutions are provided in this paper, which can effectively prevent the harmful influence of historical nihilism from human's ideologies and improve the security level of today's network environment.
Research on Peer Education Based on FIF Oral Practice APP

Based on the autonomous practice by FIF oral training app, this paper discusses the specific implementation and effect of peer education in non English major college students from the formulation of peer education plan, the establishment of team management mechanism and the selection and training of student tutors. The results show that the implementation of peer education can effectively mobilize the enthusiasm of students to practice oral English, increase their time investment in oral English practice, and promote the accuracy of pronunciation, the fluency of reading as well as the output of language. It is proved that peer education is a new way of student education, which can be applied and promoted.
RESTRICTION OF ACCESS TO INFORMATION: DOMESTIC AND FOREIGN EXPERIENCE

The results of the analysis of the restriction of access to information in libraries in Russia and abroad are presented, the characteristics of countries according to the degree of restriction of the Internet are given, the causes and methods of filtering the Internet in various countries are identified.
Optimal Maximal Entanglement EAQECCs constructed by LCD caps in PG(3,9) and PG(4,9)

Entanglement-assisted quantum error correcting codes (EAQECCs) play an important role against errors in quantum information system. Especially maximal entanglement EAQECCs can achieve a higher rate and have better noise suppression capabilities. In this paper, LCD caps in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PG</i> (3, 9) and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PG</i> (4, 9) are discussed and used to construct maximal entanglement EAQECCs with distance 4. According to Hamming bound, most of these results are optimal.
Two-Class Fuzzy Clustering Ensemble Approach Based on a Constraint on Fuzzy Memberships

In recent years, the motivation to use the hybrid mixture of various methods has been increased. In this regard, the appropriate combination of supervised or unsupervised techniques have been proposed in order to enhances the performance of classification. In this paper, in order to obtain a stable fuzzy cluster scheme, a novel ensemble approach is presented. The proposed model consists of implementations of several Fuzzy C-means (FCM) based algorithms followed by the formation of a co-association matrix in relevant with the probability of each observation belonging to the clusters. The mean of these values is combined with a restriction criterion which have been designed to perceive the exact possibility of assigning observations to clusters. In other words, certain objects receive a reward, and uncertain objects with lower fuzzy coefficient degrees tend to be ineffective. Since partitioning clustering algorithms are commonly used as a consensus function, in this study, achieved row vector is given to K-means and FCM to generate final clusters. Several datasets have been used in order to evaluate the performance of the proposed model in comparison with different methods. Specially in internal validity indices, proposed method fulfills better results than traditional algorithms.
DeepBEV: A Conditional Adversarial Network for Bird's Eye View Generation

Obtaining a meaningful, interpretable yet compact representation of the immediate surroundings of an autonomous vehicle is paramount for effective operation as well as safety. This paper proposes a solution to this by representing semantically important objects from a top-down, ego-centric bird's eye view. The novelty in this work is from formulating this problem as an adversarial learning task, tasking a generator model to produce bird's eye view representations which are plausible enough to be mistaken as a ground truth sample. This is achieved by using a Wasserstein Generative Adversarial Network based model conditioned on object detections from monocular RGB images and the corresponding bounding boxes. Extensive experiments show our model is more robust to novel data compared to strictly supervised benchmark models, while being a fraction of the size of the next best.
Teaching Research on PCO Based on Postgraduate Entrance Exam

This paper analyzes postgraduate entrance exam syllabus of Principles of Computer Organization (PCO), and summarizes the real problems of postgraduate entrance exam in recent 12 years. Then, it establishes the key content of the postgraduate entrance exam, and on this basis, formulates the theoretical teaching content and experimental content, and integrates the knowledge points and real questions into the daily teaching and process assessment in the teaching process. It improves the teaching effect of computer composition principle course, and realizes the teaching mode of both daily teaching and postgraduate entrance exam.
Endorsement just a hashtag +1 away

One of the major reasons of delayed development project deliveries is the time taken to resolve critical or nontrivial issues. Time also gets consumed in developing the Proof of Concept before final development where the area is niche, and the procedure is not known. Critical system setups also consume time. Imagine, a platform that can easily tell you if the issue you are facing has already been faced and resolved, a concept you are building using new approach has already been achieved by someone. Some tapping into the potentials of this platform can reduce project impediments and speed up the development. This paper explains in detail that a huge percentage of people who solve critical issues skip writing blogs or articles on open forums as it requires time for content writing and due to which these experts are not visible to the world for help. This forum allows the users to quickly share their knowledge without writing lengthy blogs and get recognized as experts for helping others in resolving critical issues. It also let other users to search for experts and save time for further development. We have named this forum as Endorsement - just a #tag +1 away! because via this forum getting endorsed on a skillset requires only creating a hashtag and getting an upvote on providing the right solution to the query asked.
Research on English Teaching Mode in College Based on Artificial Intelligence

College English under the traditional mode, whether in teaching content, teaching methods, teaching evaluation, etc., has been unable to adapt to the actual learning needs of current students, and the practical application of artificial intelligence in the field of education can really solve the limitation of teaching time and space. In this context, teachers need to truly realize the role of artificial intelligence in college English teaching and deeply study the new college English teaching mode under the background of artificial intelligence, so as to ensure that college English teaching objectives can be more effectively completed.
VISION AID: Scene Recognition Through Caption Generation Using Deep Learning

Visually impaired individuals heavily trust their alternative senses like acoustic signals and touch to comprehend the world outside. It is incredibly tough for a visually handicapped individual, to perceive objects without feeling them. But there could be times when physical contact between the individual and the object is risky or deadly. This proposed paper presents a real-time object recognition application to aid the visually impaired. A camera linked mobile phone with systematised orientation, given as input to a computer device for generation of real-time object detection. The proposed project utilises a convolutional neural network (CNN) to recognise pre-trained items in captured imagery and uses recurrent neural network (RNN) with LSTM for generation of captions. Here, the caption dataset is utilised for the training of captioning model. After the training, these neural models can generate captions of objects. The network output can then be analysed to impart to those with visual impairment. This is put forth in audio format by converting the generated captions to audio. Exploratory outcomes on the MS-COCO dataset show that our design beats the best in class.
Forecasting solar radiation using a deep long short-term memory artificial neural network

Abstract Solar systems are widely used to mitigate the environmental impact of the energy sector and their importance has constantly increased due to the recent EU’s strategy to lower the CO 2 emissions. Moreover, the newest Energy of Buildings Directive empathises the importance of producing energy from renewable sources to decrease the overall impact of buildings over the total end-use energy consumption. Generally, the systems’ performances are highly correlated with the incident solar radiation and outdoor air temperature. Thus, being able to accurately forecast these two parameters represents a vital step in dimensioning and maximizing the overall energy production. This Paper presents the results obtained by implementing a deep recurrent artificial neural network (ANN) trained with on year solar radiation data harvested from the UPB campus. The time series data was modelled using a special ANN architecture – the LSTM (Long Short-Term Memory) – due to its special designed internal ‘memory’ which increases its capabilities of predicting temporal sequence data. The model uses sequences of 24 hours and the resulted mean squared error (mse) for both training and validation data is under 30%.
Talking about the current situation and countermeasures of mathematics information teaching in middle schools in Gannan Tibetan area

With the continuous deepening of “Internet + education”, informatization teaching in ethnic areas has become the focus of the Ministry of Education in recent years. In order to understand the current situation of mathematics informatization teaching in middle schools in Gannan Tibetan area, the basic situation of mathematics teaching in a middle school in Gannan Tibetan area was investigated. Mainly from the two aspects of mathematics teaching content and teaching methods to analyze the factors that affect and restrict the development of mathematics informatization teaching in middle schools in Gannan Tibetan area.
Spectral recovery‐guided hyperspectral super‐resolution using transfer learning

Single hyperspectral image (HSI) super-resolution (SR) has attracted researcher's attention; however, most existing methods directly model the mapping between low- and high-resolution images from an external training dataset, which requires large memory and computing resources. Moreover, there are few such available training datasets in real cases, which prevent deep-learning-based methods from further improving performance. Here, a novel single HSI SR method based on transfer learning is proposed. The proposed method is composed of two stages: spectral down-sampled image SR reconstruction based on transfer learning and HSI reconstruction via spectral recovery module. Instead of directly applying the learned knowledge from the colour image domain to HSI SR, the spectral down-sampled image is fed into a spatial SR model to obtain a high-resolution image, which acts as a bridge between the colour image and HSI. The spectral recovery network is used to restore the HSI from the bridge image. In addition, pre-training and collaborative fine-tuning are proposed to promote the performance of SR and spectral recovery. Experiments on two public HSI datasets show that the proposed method achieves promising SR performance with a small paired HSI dataset.
An Embedded Virtual Experiment Environment System for Reality Classroom

We designed a low-cost augmented virtuality system based on the Oculus Quest to embed VR in classrooms. To build the system, we measure the size and position of tables in the classroom, make a proxy model in Unity, and then embed the proxy model to seamlessly within the real classroom. In this system, schoolchildren can realize collaborative experiments in ideal conditions or some hard-to-reach scenes. This system's contribution is: (1) By manually adding obstacles, it makes up for most VR systems that can only delimit the area but cannot identify obstacles. (2) It cleverly reuses tables and makes them play the role of anti-collision, workbench, and joystick placement. (3) It expands the available area of VR in complex environments.
Automatic Terrain Generation System Based on Path Search Optimization

In this paper, a random terrain automatic generation system is realized based on the routing optimization algorithm, which can reduce the repeated work of terrain making and avoid the restriction on too small terrain generation. The system can be used to generate and display random terrain and can be applied to map making of all kinds of games. The automatic random generation of terrain makes the game playable and exploratory according to the main plot set by the producer. We adopt Berlin noise as the seed random generator based on OpenGL to generate random path, and further to generate the random map. Compared with the existing random terrain generation, such as the creative workshop of crossing the fire line, the unmanned deep space, our model performs better in the following aspects. First, high performance. Compared with the existing methods in the same device environment, our model can increase the number of frames per second by tens. Second, high autonomy. Users can destroy and build the terrain which has been created to achieve the purpose of secondary creation. Third, high portability. Our system can generate general map files, which can be directly transplanted to unity and other existing map model processing tools. Overall, through the optimization of design and algorithm, compared with the existing terrain generation system, we have greatly improved the randomness of the map, the remolding of the play and the performance of the whole system.
The Implementation of the Algorithm of "Gluing" Images

The article is devoted to the development of a prototype of a software package for "gluing" images of various types. The original algorithm and its software implementation are proposed. The technology of data processing when "gluing" images is considered. Experimental studies of the results for different types of images were carried out.
Research of Efficient Utilization of Gymnasium in Modern Urban Development Based on Internet technology

Gymnasium is a public building with special functional requirements. Its basic nature is to serve for sports games, but it also shoulders the needs of holding large gatherings, artistic performances or exhibitions and other multi-functional applications. In this paper, combined with the modern city stadium function orientation, discusses how to use the "Internet" openness, large information capacity and rich information types, information dissemination "interactive" wait for a characteristic, at the gym resources optimization configuration, improve the utilization rate and the user experience. First of all, online service platforms such as "booking" and "selling tickets" for stadiums can be built with the help of major network platforms such as WeChat, Kuaishou and Douyin, which have a large number of users. Popularize the function introduction of the gymnasium, as well as the knowledge related to sports culture, help users to understand the dynamics and services of the gymnasium in time, and then improve the utilization rate of the gymnasium. Secondly, the needs of users learned through the "Internet" can help the venue to reasonably divide the space in advance, avoid the waste of space resources, and thus promote the stable and long-term development of the stadium on the road of sustainable development.
Dimension Selected Subspace Clustering

Subspace clustering is a popular method for clustering unlabelled data. However, the computational cost of the subspace clustering algorithm can be unaffordable when dealing with a large data set. Using a set of dimension sketched data instead of the original data set can be helpful for mitigating the computational burden. Thus, finding a way for dimension sketching becomes an important problem. In this paper, a new dimension sketching algorithm is proposed, which aims to select informative dimensions that have significant effects on the clustering results. Experimental results reveal that this method can significantly improve subspace clustering performance on both synthetic and real-world datasets, in comparison with two baseline methods.
Speech rate control methods of speech synthesis systems for quick listening

This paper proposes a speech rate control method for speech synthesis systems. This speech rate control method can produce synthesized speech that is slow at the beginning of a sentence and gradually becomes fast by the end of a sentence without changing the overall length of the synthesized speech. We conduct an experiment that compares synthesized speech with a uniform speech rate against the synthesized speech generated by the proposed method. The result of the experiment shows that the speech generated by the proposed method sounded slower than that with uniform speech rate.
Improving Reconstruction Loss Based Speaker Embedding in Unsupervised and Semi-Supervised Scenarios

Text-to-speech (TTS) models trained to minimize the spectrogram reconstruction loss can learn speaker embeddings without explicit speaker identity supervision, unlike x-vector speaker identification (SID) systems. Leveraging this way of speaker embedding learning can be useful in unsupervised or semi-supervised scenarios where non, or only some, of the training data have speaker labels. Thus, in this paper, we evaluate speaker embeddings learned by training the spectrogram prediction network under unsupervised and semi-supervised scenarios. We experimented with different data sampling strategies. The best one was sampling two different segments from the same utterance, namely A and B, where the spectrogram of B is predicted given the B phone sequence and the speaker embedding extracted from A. This method improved by 3.4% relative in EER, compared to using the same utterance for both A and B without segmenting. In the unsupervised scenario, the best speaker embedding outperformed i-vectors, the state-of-the-art unsupervised speaker embedding, in speaker verification by 12.9% relative in EER. We observed high correlation between reconstruction loss and speaker embedding quality. In the semi-supervised scenario, having more unlabeled data in training led to a better performance in speaker verification. Adding 5314 unlabeled speakers to 800 labeled speakers improved EER by 10.8 % relative.
Human Segmentation with Dynamic LiDAR Data

Consecutive LiDAR scans compose dynamic 3D sequences, which contain more abundant information than a single frame. Similar to the development history of image and video perception, dynamic 3D sequence perception starts to come into sight after inspiring research on static 3D data perception. This work proposes a spatio-temporal neural network for human segmentation with the dynamic LiDAR point clouds. It takes a sequence of depth images as input. It has a two-branch structure, i.e., the spatial segmentation branch and the temporal velocity estimation branch. The velocity estimation branch is designed to capture motion cues from the input sequence and then propagates them to the other branch. So that the segmentation branch segments humans according to both spatial and temporal features. These two branches are jointly learned on a generated dynamic point cloud dataset for human recognition. Our works fill in the blank of dynamic point cloud perception with the spherical representation of point cloud and achieves high accuracy. The experiments indicate that the introduction of temporal feature benefits the segmentation of dynamic point cloud.
The Computer for the 21st Century – Second Edition for Europe

The privacy of personal data is a human right that is systematically violated in the computing industry, according to human rights organisations. The vision that technology would help society progress through more computing and more accumulation of personal data is now 30 years old. With the knowledge of today, such a vision would be different. Instead of violating a human right, technologists could use data minimisation as a central pillar of innovation. A compelling amount of evidence shows that the majority of consumers does not feel comfortable with being tracked or profiled. This paper analyses how this vision may be realised through a variety of projects and case examples. The conclusion is that technology without tracking, personal data collection, or personal data analysis, may gradually emerge as the dominant mode of innovation and computing over the next 30 years.