Reviewerly/exHarmony huggingface dataset:
Extractive Text Summarization for Snippet Generation on Indonesian Search Engine using Sentence Transformers

Search engine results usually show a list of retrieved document titles with document summaries to give a better preview of the retrieved documents, called snippet. This research proposes extractive text summarization models to generate a snippet. A new dataset is constructed for extractive text summarization tasks using Indonesian thesis documents, in which the targeted summaries were created manually by selecting important sentences. In generating snippets, we use Lead-3 and Textrank as baselines and propose fine-tuning Sentence Transformers (SBERT). Based on the evaluation results, SBERT generated a better summary than other baselines with 0.545 Rouge-1, 0.433 Rouge-2, and 0.474 Rouge-L.
Topic Classification in Indonesian-language Tweets using Fast-Text Feature Expansion with Support Vector Machine (SVM)

Twitter is a popular social media platform that gives users the ability to send text messages with a maximum length of 280 characters which causes a lot of use of word variations that cause vocabulary writing errors and nowadays more and more tweets are spread and because of the very rapid spread it causes information overload. From the problems raised, it is necessary to be able to recognize words that have errors in writing and categorize tweets into certain categories. Therefore, this study aims to build a topic classification system on tweets that can study writing errors in a word and feature expansion using pretrained from FastText can be used to recognize writing errors in a word because the process of building word vectors from FastText can learn the internal structure of a word that will be used in the Support Vector Machine. The best results from this study get an accuracy of 76.88% with the application of feature expansion on top-1 but the application of feature expansion using pretrained classification Support Vector Machine.
Selecting Subgoal for Social AGV Path Planning by Using Reinforcement Learning

Automated Guided Vehicles (AGVs) can be widely used in many applications. In a dynamic environment where humans and machines coexist, it is expected to encounter traffic jams, queuing, overtaking, comity, and many other diverse, dynamic social interaction. However, capturing the characteristics of the social interactions among passages for path planning is a complex problem. It is crucial to realize the application of AGVs. Dealing with such a complex dynamic environment thus becomes a primary subject of navigation. However, in facing stop-and-go crowds in shopping malls or traffic jams, the past navigation methods cannot effectively cope with the navigation task. Therefore, we propose a hierarchical reinforcement learning framework named MTON, which collects historic environmental statistics to analyze social interactions and periodically selects a promising navigation subgoal to complete the navigation. Our model introduces the idea of interchanging between waiting and moving. Thus it can cope with stop-and-go dynamic obstacles and find a way to break through the barriers to the final goal.
Feature Expansion with Word2Vec for Topic Classification with Gradient Boosted Decision Tree on Twitter

Online Social Networks have an essential role as a source of information, especially during an emergency. One of them is Twitter, a service that allows users to send and read messages but is limited in character. Thus, tweets that are written are very short and do not always use the correct grammar and use many variations of words. Using word variations can increase the likelihood of vocabulary mismatches and make tweets difficult to understand. One solution to overcome this problem is to expand the features of the tweet. The feature expansion on Twitter is a semantic addition to the process of multiplying the original text to make it look like large text. In this study, Word2Vec will be used with the Gradient Boosted Decision Tree Method to classify it. The expected result of this research is to reduce words or sentences in the classification of Twitter topics which are evaluated using the accuracy value, F1-Measure. The highest accuracy value in the application of feature expansion using Word2Vec with the Gradient Boosted Decision Tree classification method is 85.44%.
Privacy-Preserving Presence Tracing for Pandemics Via Machine-to-Machine Exposure Notifications

At the onset of Covid-19 several Mobile Contact Tracing Applications (MCTA) were deployed and in many cases contributed to curbing the pandemic by triggering Exposure Notifications (EN) to users who were in proximity to infected users. Recently, a number of MCTA were enhanced with Digital Presence Tracing (DPT) functionality in an effort of the public health authorities to break infection chains mostly in indoor crowded spaces and manage super-spreading events (e.g., concerts, parties). That is, alerting individuals who visited the same place or attended the same event with infected users. This is typically implemented by scanning a QR code at the venue entrance. In this work, we present a DPT solution that relies on EN-Hubs, i.e., Bluetooth-enabled IoT devices, that propagate EN in a machine-to-machine fashion reaching all visitors/attendants seamlessly through their MCTA. The proposed solution removes the overhead of issuing, managing, and scanning QR codes every time people visit a place. In addition, it can be conveniently retrofitted to existing nation-wide MCTA offering DPT capabilities with limited implementation cost.
A First Look at the ACM/IEEE-CS/AAAI Computer Science Curricula (CS202X)

A Joint Task Force of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers~-~Computer Society (IEEE-CS), and Association for the Advancement of Artificial Intelligence (AAAI) was constituted in early 2021 to begin the decennial process of revising the Computer Science curricular guidelines, which were last released as Computer Science Curricula 2013 (CS2013). This special session will present the first draft of the revised curricular guidelines, currently referred to as CS202X, and solicit feedback. The CS202X draft will include revisions to CS2013 Knowledge Areas, a proposed competency model being incorporated into the curricular guidelines, and other updates. Targeted towards educators, administrators and others interested in Computer Science curricular issues, this session will be led by the co-chairs and members of the CS202X Steering Committee as part of their process to engage the community and solicit feedback.
Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion

In recent years, under the background of stable economic operation, people's research on economic systems has become increasingly popular. The dynamic input-output model reflects the change and development process of the input-output relationship of the economic system over a period of time. The main purpose of tracking control is to design a suitable controller so that the output of the control system can track the output of the reference system as much as possible. In the economic system, data is an important factor. Based on this, this paper mainly studies the tracking control of dynamic input-output economic system based on data fusion. This research takes the data fusion of the dynamic input-output economic system as the starting point and takes the optimal control and tracking of the economic system as the research object of this research. Based on data fusion technology, a new dynamic input-output economic system tracking and control is proposed. This paper studies the finite-time optimal tracking control of linear systems. Through numerical examples, comparing the finite-time and infinite-time optimal control simulation results, it is proved that the algorithm can achieve good tracking control. Experimental data shows that the optimal and suboptimal performance indicators for a limited time are 0.7729412 and 1.5687310, respectively. Therefore, compared with the infinite-time optimal control, the performance loss and the final tracking error of the suboptimal control proposed in this study are reduced.
Real-Time Detection of Insulators and Drop Fuses Based on Improved YOLOv4

During the power grid system maintenance and overhaul, real-time detection of the insulators and drop fuses is important for the live working robots in the distribution network to plan motion. The visual system of the robot needs object detection algorithms with high detection precision, fast speed, and robustness to image brightness changes. In this paper, the improved YOLOv4 is proposed for detecting the insulators and drop fuses based on the YOLOv4. The improved YOLOv4 extracts features of power components through convolutional neural networks (CNN) and then performs feature fusion. After feature extraction and fusion, the algorithm generates prediction boxes based on anchor boxes that are clustered by fuzzy C-means algorithm (FCM) instead of K-means algorithm to detect the objects. Finally, the nonmaximum suppression algorithm (NMS) is used to obtain the final prediction results. In order to detect small targets, the improved YOLOv4 is added to a larger detection layer. For enhancing the robustness of the algorithm, data augmentation methods are carried out to enrich the data set. Combining the improvements, the test results show that the improved YOLOv4 gets higher accuracy and faster detection speed compared with the other detection algorithms based on deep learning. The mean average precision is 97.0%, and the average detection time is 0.012 s. Therefore, the improved YOLOv4 is suitable for the live working robots in the distribution network to detect the insulators and drop fuses fast and accurately.
Cross-Modal Manifold Propagation for Image Recommendation

The growing complex user intention gap and information overload are obstacles for users to access the desired content. User interactions and the involved content indicate rich evidence of users’ interests. It is required to investigate interaction characters over user interest and information distribution, and this alleviates information overload for personalized recommendation. Therefore, this work explores user interests with interactions and visual information from users’ historical records for image recommendation. This paper introduces cross-modal manifold propagation (CMP) for personalized image recommendation. CMP investigates the trend of user preferences by propagating users’ historical records along with users’ interest distribution, which produces personalized interest-aware image candidates according to user interests. CMP simultaneously leverages visual distribution to spread users’ visual records relying on the dense semantic visual manifold. Visual manifold propagation estimates detailed semantic-level user-image correlations for ranking candidate images in recommendations. In the proposed CMP, both user interest manifold and images’ visual manifold compensate each other in propagating users’ records to predict users’ interaction. Experimental results illustrate the effectiveness of collaborative user-image propagation of CMP for personalized image recommendation. Performance improved by more than 20% compared to that of existing baselines.
Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection

Pedestrian detection is a great challenge, especially in complex and diverse occlusion environments. When a pedestrian is in an occlusion situation, the pedestrian visible part becomes incomplete, and the body bounding box contains part of the pedestrian, other objects and backgrounds. Based on this, we attempt different methods to help the detector learn more features of the pedestrian under different occlusion situations. First, we propose region resolution learning, which learns the pedestrian regions on the input image. Second, we propose fine-grained segmentation learning to learn the outline and shape of different parts of pedestrians. We propose an anchor-free approach that combines a pedestrian detector CSP, region Resolution learning and Segmentation learning (CSPRS). We help the detector to learn extra features. CSPRS provides another way to perceive pixels, outline and shapes in pedestrian areas. This detector includes region resolution learning, and segmentation learning helps the detector to locate pedestrians. By simply adding the region resolution learning branch and segmentation branch, CSPRS achieves good results. The experimental results show that both methods of learning pedestrian features improve performance. We evaluate our proposed detector CSPRS on the CityPersons benchmark, and the experiments show that CSPRS achieved 42.53% on the heavy subset on the CityPersons dataset.
Basic human requirements of physical and virtual spaces and their implications

The article portrays, first, the basic human requirements of physical and virtual spaces. Second, the article attempts to examine the relationships among the several basic human requirements of physical space, as well as the relationships between the requirements of physical space and those of virtual space. Third, the article suggests that the basic spatial requirements of individuals be examined in light of human spatial pulses, which include each person's fixed origin, a movement, and a fixed destination, with implications for people, their activities, and places. Discussions begin with the basic human requirements of physical space, fixity and mobility, and the human needs, which they present. This is followed by assessments of the relationships between these requirements, turning next to virtual space, attempting to expose the two human basic requirements of virtual space, access and connectivity, and the relationships between these two basic requirements. This is followed by an assessment of the relationships between the human requirements of physical and virtual spaces, and their implications for human activity. Finally, a joint interpretation of human spatial requirements and their activities, through the notion of human spatial impulses, is suggested.
DETERMINATION OF URANIUM AND THORIUM SURFACE DISTRIBUTION IN GEOLOGICAL SAMPLES: COMPARISON OF TABLETOP MACRO AND MICRO-XRF SCANNING

This paper describes the identification and quantification of uranium, thorium and other chemical elements in a low-grade uranium ore, using macro and micro-X-ray fluorescence scanning. The result of such scanning is their surface distribution determined in flat samples. The basic parameters of both setups are described. The investigation was focused mainly on the improvement of the lateral resolution in the laboratory tabletop macro-XRF and its comparison with micro-XRF scanning. A standard reference material NIST 610 was used as a homogeneous reference silicate material of a known composition. The measurements have demonstrated how the macro-XRF with a quite wide X-ray beam can be competitive with the micro-XRF scanning. The capabilities of both devices were established, utilizing measurements of selected uranium-bearing sediments samples, from the Břevniště deposit in the Czech Republic.
IoT based cyber forensics in big data optimization and privacy using deep neural anomaly detection with Hadoop clustering and convolution based Adam optimizer

Abstract The reliability of the digital forensic system mainly lies in its ability to detect the attack on time and minimize the security risk associated with the Internet of Things (IoT) device. The existing machine learning architectures mainly suffered from different complexities while processing the attack features such as increased processing time, high manual intervention, complexity to handle large datasets, and low prediction accuracy. To overcome these issues, this article proposes a novel deep learning architecture for data forensics. The low generalization capability of the neural network is handled via the Hadoop clustering and convolution‐based ADAM (CADAM) optimizer approach for improving the optimization capability of the model. In this way, the privacy of the model is improved. The efficiency of the proposed CADAM optimized deep neural network architecture is evaluated by using the UNSW‐NB15 and Bot‐IoT datasets. The proposed model offers improved performance in terms of runtime, precision, sensitivity, specificity, latency, cost, ROCAUC curve, and anomalous traffic when compared with the existing techniques. From the experiments carried out, we can conclude that the proposed framework revealed higher performance to discover and prevent cyber‐attacks against other conventional approaches.
Micro-Attention Branch, a Flexible Plug-In That Enhances Existing 3D ConvNets

In the field of video motion recognition, with the increase in network depth, there is an asymmetry in the amount of parameters and their accuracy. We propose the structure of micro-attention branches and a method of integrating attention branches in multi-branches 3D convolution networks. Our proposed attention branches can improve this asymmetry problem. Attention branches can be flexibly added to 3D convolution network in the form of plug-in, without changing the overall structure of the original network. Through this structure, the newly constructed network can fuse the attention features extracted by attention branches in real time in the process of feature extraction. By adding attention branches, the model can focus on the action subject more accurately, so as to improve the accuracy of the model. Moreover, in the case that there are multiple sub-branches in the construction module of the existing network, 3D micro-attention branches can well adapt to this scenario. In the kinetics dataset, we use our proposed micro-attention branch structure to construct a deep network, which is compared with the original network. The experimental results show that the recognition accuracy of the network with micro-attention branches is improved by 3.6% compared with the original network, while the amount of parameters to be trained is only increased by 0.6%.
Calibration of Double-Plane-Mirror Catadioptric Camera Based on Coaxial Parallel Circles

A catadioptric camera with a double-mirror system, composed of a pinhole camera and two planar mirrors, can capture multiple catadioptric views of an object. The catadioptric point sets (CPSs) formed by the contour points on the object lie on circles, all of which are coaxial parallel. Based on the property of the polar line of the infinity point with respect to a circle, the infinity points in orthogonal directions can be obtained using any two CPSs, and a pole and polar pair with respect to the image of the absolute conic (IAC) can be obtained through inference of the Laguerre theorem. Thus, the camera intrinsic parameters can be solved. Furthermore, as the five points needed to fit the image of a circle are not easy to obtain accurately, only sets in which five points can be located can be obtained, whereas the points on the line of intersection between the two plane mirrors and the ground plane can easily be obtained accurately. An optimization method based on the analysis of neighboring point sets to compare the intersection points with an image of the center of multiple circle images fitted using the point sets is proposed. Bundle adjustment is then applied to further optimize the camera intrinsic parameters. The feasibility and validity of the proposed calibration methods and their optimization were confirmed through simulation and experiments. Two primary innovations were obtained from the results of this study: (1) by applying coaxial parallel circles to the double-plane-mirror catadioptric camera model, a variety of calibration methods were derived, and (2) we found that the overall model could be optimized by analyzing the features of the neighboring point set and bundle adjustment.
User‐centered approaches in software development processes: Qualitative research into the practice of Hungarian companies

Abstract Integrating user‐centered approaches into development processes is one of the main challenges nowadays that derives from different objectives of software engineering (SE) and human‐computer interaction (HCI) fields. For SE experts, the main goal is quality code creation, whereas for HCI professionals, it is the continuous product interaction with the users. The major question is what tools and timings can be used together to achieve these goals effectively. Therefore, this article provides comparative, exploratory, and qualitative research about possible solutions on how practitioners transfer HCI values and practices to SE processes. The current practice of software companies was studied by conducting interviews on a sample of 13 Hungarian Information Technology companies to explore the SE processes in respect of several dimensions (applied development models, the integrity of user‐centered methods, and the user experience [UX] maturity). According to preliminary expectations, the development processes of the various companies proceed in different steps; nevertheless, they can be well grouped together based on the UX methods applied. The results representing the various user‐centered processes can be considered useful for future decision makers of software companies worldwide.
Achieving Optimal Value of Solar: A Municipal Utility Rate Analysis

Wind and solar renewable energy in the United States is projected to triple by 2050 to nearly 30% of total electric energy generation. The upper Midwest region (Iowa, Minnesota, and North and South Dakota in particular) is considered wind energy country and not historically known for solar energy development. In this work, Value of Solar (VOS) is developed as a photovoltaic (PV) optimization measure and analysis tool using a northwest Iowa municipality as a representative case study. By applying a top-down load duration curve system analysis, VOS is used to optimize PV orientation and compare electric rate structures for increasing levels of total PV energy contribution. VOS of a fixed south-southwest orientation exceeds the levelized annual costs of installation with a larger net benefit than a one-axis-tracking solar system. Production-data modeled VOS is up to 12% higher than Typical Meteorological Year (TMY) predictions, indicating significant correlation between PV generation and peak municipal demand. Compared to alternative time-of-use rates, a demand/energy rate structure better matches VOS economic value and optimal orientation. This VOS methodology is an easy-to-use yet meaningful tool for municipalities and smaller utilities to evaluate strategic installation of and investment in PV for their local community.
A Diversified Integration Method of IPE Teaching Resources Based on Ant Colony Algorithm

In the era of big data, cloud computing, and machine learning, this has become essential to promote the better development of ideological and political education (IPE) in institutions and universities. In fact, we must pay close devotion to the integration and utilization of online teaching resources, take full benefits of the assistances of big data, machine learning, and continuously collect and sort resources that are conducive to IPE in higher vocational academies, so as to optimize the educational process. In fact, the resource allocation within the context of the IPE is not well-addressed in the existing literature; and the allocation of resources is quite unreasonable. In higher vocational education, the form and content of the IPE will enhance its effectiveness. In this paper, we use the ant colony algorithm to efficiently obtain the solution set for resource allocation, thereby addressing the issues of unreasonable allocation of IPE resources and inefficient testing. In addition, the local search method is incorporated into the ant colony optimization technique to perform a local search on the solution set of the obtained resource allocation in order to increase the algorithm’s performance. On the standard test set, algorithm comparison experimentations are carried out to validate the efficacy and efficiency of the suggested algorithm.