Reviewerly/exHarmony huggingface dataset:
Personal Voice Assistant: from Inception to Everyday Application

The creation of automatic speech recognition systems is a popular trend in the development of information technology. These technologies are developing at a very fast pace, gradually covering more and more areas: already now we can say how firmly they have settled in our lives. The term "speech technologies" means a fairly large layer of information technologies, but one of the most advanced products in this area is a voice assistant, which includes the use of all types of speech technologies: speech recognition, speech synthesis, a system for developing and analyzing voice information, and voice biometrics. A voice assistant is software that allows you to control your device using voice commands. The range of possibilities does not end with the execution of commands; a modern assistant is even able to maintain a conversation with the user. Since the voice assistant is a complex innovation that consists of many different technologies, the task of a smart assistant is to ensure that they work smoothly with each other. Now there are many voice assistants on the market that can make life easier for a person. Now you don’t have to manually enter a question into a search engine or search for a song, you just need to say what you want, and the voice assistant will find everything on its own. But every year there are more and more assistants and it becomes more and more difficult for the average user to choose, because each assistant has its own characteristics. Nevertheless, the daily use of this technology is becoming more widespread. Another area that is rapidly developing is gaming. Using more and more innovations, the next logical step is to implement a voice assistant, at least at the training stage. However, real progress is out of the question for the time being.
Novelty in Image Classification

In this chapter, we introduce real-world data in the form of RGB images and expand on the application of the underlying theory. We divide the world of images into known and novel sets and then use a sampling process to generate a large number of novelty experiments. There is a learning-based novelty-aware classification agent, but it does not actively interact with the world. For the image classification task, the perceptual operators are defined as the features computed from a Deep Neural Network (DNN) trained on the classification task using the known classes.
Web-Based Company Workshop Tool Control System (Case Study of Dipo Kereta Malang)

The management of existing tools at Dipo Kereta Malang in the process of borrowing and returning still uses conventional methods, where employees write in the available books. This can cause problems including a lot of unrecorded data, when at the same time the officer is doing other work. Then, when the process of searching for tool data in a book that takes time. In addition, officers will always check the availability of tools before borrowing. As the role of information and communication technology and the development of tool lending applications to support companies to be more effective. Information technology used includes computer technology to support the loan information system. From the description of the problems above, a web-based tool control system will be created. Website which can be interpreted as a form of group of web pages which can be accessed by everyone and are bound together in one domain. This web-based system can generate information in the form of tool availability, loan reports, and changes in the tool borrowing process where tool borrowing is done the previous day for tomorrow's work. For this reason, employees no longer need to make manual records which are considered less effective and efficient in the future. And with the system created can improve previous shortcomings.
Web-based Information System for Climbing Equipment Rental (Case Study of Camp 2 Shop)

The business of renting hiking equipment is currently very much loved by entrepreneurs. Due to the fact that the content business generates a lot of rupiah coffers, nowadays many young people are channeling their hobbies to mountain climbing. Regardless of age, there are also many ambitious fathers who take the time to climb. No wonder hiking equipment rentals are selling well at this time.Unfortunately, the rental of hiking equipment is still traditional, namely by coming to the store and choosing the items to be rented, with a system like this it is likely that the items to be rented are empty or are being rented by other tenants. This is quite detrimental to customers who have come all the way and hope that the item they are looking for is there. Not only customers who are harmed here, shop owners will also lose many customers.Therefore, a web-based information system is needed that can be accessed by potential customers, with this information system it is hoped that it can assist customers in renting hiking equipment without manual processes.
A bibliometric analysis of Agile software development publications originating from Turkey

Abstract Agile software development has reached wide adoption in various countries including Turkey, even though from which its original cultural backgrounds differ. In Turkey, many organizations have started to adopt Agile approaches more and more in their software development processes. This interest in the country's software development is parallel to what the academic literature on Agile in the country exhibits. However, despite the prevalence of Agile in Turkey, there is a lack of sufficient secondary research and comprehensive review on Agile in Turkey, which poses a significant necessity for further investigation. Considering this gap, we performed a quantitative bibliometric analysis of Agile software development publications produced by Turkish organizations in a holistic and broad approach both for scholars and practitioners. We provide a summary of relevant academic studies that emerged in Agile research in Turkey by focusing on many aspects including bibliometric properties of papers, researchers, affiliations, venues, and thematic contents that are separated into 15 sub‐research questions. After delivering results based on the questions, we discuss the results and findings of our study and present implications regarding the findings. The main contributions of our work are twofold. First, the paper may help the readers to have a quick idea, understand the subject, and gain insight from a large volume of scientific data. Second, the paper can help readers to use these analyses to form future research.
Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates

Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value. Currently, mainstream grammar-correction methods primarily rely on sequence labeling and text generation, which are two kinds of end-to-end methods. These methods have shown exemplary performance in areas with low error density but often fail to deliver satisfactory results in high-error density situations where multiple errors exist in a single sentence. Consequently, these methods tend to overcorrect correct words, leading to a high rate of false positives. To address this issue, we researched the specific characteristics of the Chinese grammar error correction (CGEC) task in high-error density situations. We proposed a grammar-correction method based on prompt templates. Firstly, we proposed a strategy for constructing prompt templates suitable for CGEC. This strategy transforms the CGEC task into a masked fill-in-the-blank task compatible with the masked language model BERT. Secondly, we proposed a method for dynamically updating templates, which incorporates already corrected errors into the template through dynamic updates to improve the template quality. Moreover, we used the phonetic and graphical resemblance knowledge from the confusion set as guiding information. By combining this with BERT’s prediction results, the model can more accurately select the correct characters, significantly enhancing the accuracy of the model’s prediction correction results. Our methods were validated through experiments on a public grammar-correction dataset. The results indicate that our method achieves higher correction performance and lower false correction rates in high-error density scenarios.
Adaptive path planning for unknown environment monitoring

The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM &amp; A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.
Outlier detection for incomplete real-valued data via rough set theory and granular computing

Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method.
The Weights Reset Technique for Deep Neural Networks Implicit Regularization

We present a new regularization method called Weights Reset, which includes periodically resetting a random portion of layer weights during the training process using predefined probability distributions. This technique was applied and tested on several popular classification datasets, Caltech-101, CIFAR-100 and Imagenette. We compare these results with other traditional regularization methods. The subsequent test results demonstrate that the Weights Reset method is competitive, achieving the best performance on Imagenette dataset and the challenging and unbalanced Caltech-101 dataset. This method also has sufficient potential to prevent vanishing and exploding gradients. However, this analysis is of a brief nature. Further comprehensive studies are needed in order to gain a deep understanding of the computing potential and limitations of the Weights Reset method. The observed results show that the Weights Reset method can be estimated as an effective extension of the traditional regularization methods and can help to improve model performance and generalization.
Two stream deep neural network based framework to detect abnormal human activities

In recent times, the volume of video surveillance data has been rapidly increasing, and processing is required to detect various kinds of activities in these data. Most human activity recognition methods primarily focus on detecting normal human activities; however, identifying and detecting suspicious activities is equally important. We propose a two-stream framework for detecting abnormal human activities in real-time videos. The proposed method uses the first stream to extract features related to spatial characteristics and the other stream to extract the temporal features from the videos. The spatial features focus on the contextual details in the frame, including color, texture, and other robust features. The temporal features focus on the features related to the changes in frames with respect to the time, which play a vital role in the detection of activity. The proposed framework leverages the Xception model to extract the features in the spatial and temporal streams. The extracted features are integrated for further processing and detection. To process the integrated features and detect the activity, we train a multi-layer bidirectional long short term memory network using the extracted features. The proposed framework is trained in an end-to-end manner to accurately estimate human activity by learning patterns and forming correlations. The effectiveness of the proposed approach is evaluated by assessing the popular UCF Crime dataset, which encompasses a variety of criminal activities, and using performance measures such as recall, precision, and accuracy. Experimental results indicate that the proposed approach outperforms other contemporary methods, achieving an accuracy of 86%.
Dynamics-aware loss for learning with label noise

Label noise poses a serious threat to deep neural networks (DNNs). Employing robust loss functions which reconcile fitting ability with robustness is a simple but effective strategy to handle this problem. However, the widely-used static trade-off between these two factors contradicts the dynamics of DNNs learning with label noise, leading to inferior performance. Therefore, we propose a dynamics-aware loss (DAL) to solve this problem. Considering that DNNs tend to first learn beneficial patterns, then gradually overfit harmful label noise, DAL strengthens the fitting ability initially, then gradually improves robustness. Moreover, at the later stage, to further reduce the negative impact of label noise and combat underfitting simultaneously, we let DNNs put more emphasis on easy examples than hard ones and introduce a bootstrapping term. Both the detailed theoretical analyses and extensive experimental results demonstrate the superiority of our method.
CotNet target tracking algorithm based on attention mechanism and context-awareness

In recent years, Siamese network algorithms based on deep learning classes have achieved better tracking accuracy and speed and become one of the research hotspots in the field of target tracking. However, the traditional Siamese network algorithm lacks a holistic view of the target and extracts shallow features, making it easy to lose track of the target in complex environments. The paper proposes a Contextual transformer network for visual recognition (CotNet) target tracking algorithm based on attentional mechanisms and contextual awareness to address this. The paper innovatively uses the CotNet50 network as the backbone network and adopts a residual network variant design scheme with a self-attention mechanism, which can enhance the feature representation capability of the network model and improve the performance of the algorithm. In addition, to handle changes in appearance during target tracking, an efficient channel attention module, and a global contextual feature module are embedded in the backbone network branch to enhance the network's overall perception of the target and improve the algorithm's tracking accuracy. The experimental results of this paper's algorithm on the VOT2018 data show that the accuracy, robustness, and EAO (Expected Average Overlap) are improved by 7.3%, 13.95%, and 11.9% respectively compared to SiamFC. It has good tracking results when dealing with complex scenes on the OTB100 dataset.
Research on unmanned transfer vehicle path planning for raw grain warehousing

A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5×5 field search way is used instead of the traditional 3×3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment.
New Problems in Port-and-Sweep Solitaire

SummaryPort-and-Sweep Solitaire (PaSS) is a puzzle game related to the classic pastime of peg solitaire. One of the great surprises of peg solitaire is that its analysis is closely tied to mod 2 arithmetic, and PaSS has an analogous relation to mod 3 arithmetic, which we explain in the article. We give several families of challenging PaSS puzzles for the reader to solve and demonstrate typical techniques for identifying problems which cannot be solved. Additional informationNotes on contributorsJacob A. Siehler Jacob Siehler (jsiehler@gustavus.edu) is a professor of mathematics at Gustavus Adolphus College, with a Ph.D. from Virginia Tech. He enjoys teaching students of all ages, and particularly enjoys making problems, puzzles, and games for others to solve, play with, and enjoy.
GGTr: An Innovative Framework for Accurate and Realistic Human Motion Prediction

Human motion prediction involves forecasting future movements based on past observations, which is a complex task due to the inherent spatial-temporal dynamics of human motion. In this paper, we introduced a novel framework, GGTr, which adeptly encapsulates these patterns by integrating positional graph convolutional network (GCN) layers, gated recurrent unit (GRU) network layers, and transformer layers. The proposed model utilizes an enhanced GCN layer equipped with a positional representation to aggregate information from body joints more effectively. To address temporal dependencies, we strategically combined GRU and transformer layers, enabling the model to capture both local and global temporal dependencies across body joints. Through extensive experiments conducted on Human3.6M and CMU-MoCap datasets, we demonstrated the superior performance of our proposed model. Notably, our framework shows significant improvements in predicting long-term movements, outperforming state-of-the-art methods substantially.
ANAS: Asymptotic NAS for large-scale proxyless search and multi-task transfer learning

Neural Architecture Search (NAS) is an emerging solution to design a lightweight network for researchers to obtain a trade-off between accuracy and speed, releasing researchers from tedious repeated trials. However, the main shortcoming of NAS is its high and unstable memory consumption of the search work, especially for large-scale tasks. In this study, the proposed Asymptotic Neural Architecture Search network (ANAS) achieved a proxyless search for large-scale tasks with economic and stable memory consumption. Instead of proxy search like other NAS algorithms, ANAS achieved the large-scale proxyless search that directly learns deep neural network architecture for target task. ANAS reduced the peak value of memory consumption by an asymptotic method, and kept the memory consumption stable by the linkage change of a series of key indexes. A pruning operation and efficient candidate operations decreased the total memory consumption. Finally, ANAS achieved a good trade-off between accuracy and speed for classification tasks on CIFAR-10, CIFAR-100, and ImageNet datasets. Besides, except for the classification task, it achieved excellent multi-task transfer learning ability for implementing the segmentation task on CamVid and Cityscapes. ANAS reached 22.8% test errs with 5 M parameter on ImageNet, and 72.9 mIoU (mean Intersection over Union) with 119.9 FPS (Frames Per Second) on Cityscapes dataset.
Predicting anticancer drug sensitivity on distributed data sources using federated deep learning

Drug sensitivity prediction plays a crucial role in precision cancer therapy. Collaboration among medical institutions can lead to better performance in drug sensitivity prediction. However, patient privacy and data protection regulation remain a severe impediment to centralized prediction studies. For the first time, we proposed a federated drug sensitivity prediction model with high generalization, combining distributed data sources while protecting private data. Cell lines are first classified into three categories using the waterfall method. Focal loss for solving class imbalance is then embedded into the horizontal federated deep learning framework, i.e., HFDL-fl is presented. Applying HFDL-fl to homogeneous and heterogeneous data, we obtained HFDL-Cross and HFDL-Within. Our comprehensive experiments demonstrated that (i) collaboration by HFDL-fl outperforms private model on local data, (ii) focal loss function can effectively improve model performance to classify cell lines in sensitive and resistant categories, and (iii) HFDL-fl is not significantly affected by data heterogeneity. To summarize, HFDL-fl provides a valuable solution to break down the barriers between medical institutions for privacy-preserving drug sensitivity prediction and therefore facilitates the development of cancer precision medicine and other privacy-related biomedical research.