Reviewerly/exHarmony huggingface dataset:
Grouping 2 Hour Load Data Substation Using Hierarchical Clustering

Abstract In this modern era, information technology is very influential in many ways, especially in the business and business world. Computer is a device that is needed for data processing, so the data processed can be effectively and efficiently to provide information needed by individuals, companies, and agencies. Clustering is a method for finding and grouping data that has similarity between one data and others. In the clustering algorithm, data will be grouped into clusters based on the similarity of one data to another. The principle of clustering is to maximize the similarity between members of one cluster and minimize similarities between different cluster members. The most well-known category of clustering algorithms is Hierarchical Clustering. As well as PLN ( State Electricity Company ), where PLN is a state-owned enterprise ( BUMN ) that provides electricity services to all communities and all circles, both private and business entities whose job it is to serve users in electricity. Services will be maximized and can be accessed by all parties if in the service there is a role for the media as a assistant in conveying information to all circles. This information will be right on the target if the information is processed from a reliable source. PT PLN ( Persero ) Krueng Geukuh did not yet have a load data collection system that makes staff still did data collection by manual methods. The result of this study shows the system of 2 hour load cluster data grouping using hierarchical clustering of PT PLN (Persero) Krueng Geukuh is an effective solution to help staff record computerized loads and can increasing the work performance in data collection. Designing systems of Grouping Data Load 2 Hours Substation Using Hierarchical Clustering PT PLN ( persero ) Krueng Geukuh was built by using PHP so it has a more attractive appearance and uses a MySQL database so that load managers become more effective.
Countering Inconsistent Labelling by Google’s Vision API for Rotated Images

Google’s Vision API analyses images and provides a variety of output predictions, one such type is context-based labelling. In this paper, it is shown that adversarial examples that cause incorrect label prediction and spoofing can be generated by rotating the images. Due to the black-boxed nature of the API, a modular context-based pre-processing pipeline is proposed consisting of a ResNet50 model that predicts the angle by which the image must be rotated to correct its orientation. The pipeline successfully performs the correction whilst maintaining the image’s resolution and feeds it to the API which generates labels similar to the original correctly oriented image, and using a percentage error metric, the performance of the corrected images as compared to its rotated counterparts is found to be significantly higher. These observations imply that the API can benefit from such a pre-processing pipeline to increase robustness to rotational perturbances.
Test Reporting at a Large-Scale Austrian Logistics Organization: Lessons Learned and Improvement

Context and Background. Software testing and test automation are important activities in software development where frequent requirements changes and the fast delivery of software increments are supported by traditional and agile development processes. Test reports are often used as “proof of evidence” for executed software tests. However, the practical impact of test reports, such as decision making and quality assessment, requires structured information which might not be available in sufficient quality. Goal. In this paper we (a) report on needs of test reports of different stakeholders at a large-scale Austrian logistics organization, (b) develop candidate improvement actions based on the state of the practice, and (c) conceptually evaluate selected improvement actions. Method. We used surveys and interviews to elicit needs and expected capabilities for test reporting and developed candidate improvement. We used expert discussions prioritize improvement actions in the organization context for further implementation. Results. Based on 23 recommended improvement actions, 14 were initially selected for implementation. Most of these accepted improvement action focus on regular test status reports and visualization aspects of test reports. Conclusion. Although test reporting is systematically applied in development processes, there is still some potential to improve test reports to gain (additional) benefits for related stakeholder.
Excavating effective information in different stage of backbone to improve semantic segmentation results

Abstract To overcome the two difficulties in semantic segmentation task:1) the presence of multi-scale objects, 2) the loss of spatial resolution, a new semantic segmentation model is designed in this paper, which can explore effective information from different stage of backbone. Extensive experiments conducted on two public benchmark datasets have proved the effectiveness of each module, and the new model achieves 73.62% mIoU on Cityscapes test set and 79.88% mIoU on PASCAL VOC 2012 test set.
Neural Networks as Model Selection with Incremental MDL Normalization

If we consider the neural network optimization process as a model selection problem, the implicit space can be constrained by the normalizing factor, the minimum description length of the optimal universal code. Inspired by the adaptation phenomenon of biological neuronal firing, we propose a class of reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space under the Minimum Description Length (MDL) principle. We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization. The empirical results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision and reinforcement learning tasks. As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform. Further research is proposed to discover these scenarios and explore the behaviors among different variants.
Search for rectangle distinguisher 16 round on LBlock

LBlock is a lightweight block cipher algorithm recomended by Wu and Zhang in ACNS 2011. LBlock algorithm was claimed to be resistant to differential cryptanalysis. In 2013, Chen and Miyaji attacked the LBlock algorithm with boomerang (rectangle) attack on 18 round using 16 round rectangle distinguisher by applying ladder switch technique and multiple differential. Chen and Miyaji found the probability of the rectangle distinguisher to be 2−60.4298 and required the plaintext pair of 262.2649 to obtain a right quartet, which did not clearly explain the process in detail. In this research, we try to find Chen and Miyaji’s 16-round rectangle distinguisher and other possible 16-round rectangle distinguishers which have at least the same probability. This study uses restrictions on the threshold used in the branch and bound technique for the first path E0 is 4 and for the second path E1 is 2, and the study uses the assumption that the result of XOR operation between two active nibbles is a passive nibble. Based on the experiment by applying the ladder switch technique, a rectangle distinguisher with bigger probability than Chen and Miyaji was found, that is 2−54.322028, by using distinguisher (000020b0 12100000 → 00044400 00300004). The 259.171663 plaintext pairs are required to recover the key.
Similar Group Finding Algorithm Based on Temporal Subgraph Matching

The similar group search is an important approach for the recommendation system or social network analysis. However, there is a negligence of the influence of temporal features of social network on the search for similarity group. In this paper, we model the social network through the temporal graph and define the similar group in the temporal social network. Then, the T-VF2 algorithm is designed to search the similarity group through the temporal subgraph matching technique. To evaluate our proposed algorithm, we also extend the VF2 algorithm by point-side collaborative filtering to perform temporal subgraph matching. Finally, lots of experiments show the effectiveness and efficient of our proposed algorithm.
Wikitheoria

In the social sciences, theory construction refers to the research process of building testable scientific theories to explain and predict observed phenomena in the natural world. Terms represent the theories' concepts or ideas and their meanings are explicated in their definitions. The principle of parsimony, an important criterion for evaluating the quality of theories (e.g., as exemplified by Occam's Razor) mandates that we minimize the number of definitions (terms) used in a given theory. Conventional methods for parsimony analysis in theory construction are based on the heuristic approaches. However, it is not always easy for young researchers to understand the theoretical work in a given area because of the problem of "tacit knowledge", which often makes results lack coherence and logical integrity. Therefore, we propose a generic knowledge aggregation framework to facilitate the parsimonious approach of theory construction with a cloud-based theory modularization platform and semantic-based algorithms to minimize the number of definitions. The proposed approach is demonstrated and evaluated using the modularized theories from the database and sociological definitions retrieved from the system lexicon and sociological literature. The experiment results showed that the proposed approach achieves the precision of 82%, recall of 82% and accuracy of 81.69%. This study proves the effectiveness of using cloud-based knowledge aggregation system and semantic analysis models for promoting the parsimonious sociology theory construction.
Designing an Ontology for Semantic Integration of Various Conceptual Models

Ontologies and conceptual modelling are very close areas in software engineering. This paper is focused on initial steps towards the integration of conceptual models by the foundation of Ontology for Conceptual Models Integration to capture the knowledge about various conceptual modelling languages, including process, event, and object-role modelling. It is based on previous work in this area and has an ambitious goal to allow semantic integration of conceptual models made in different languages to cover more aspects and details of the problem domain. The presented contribution consists of the related work research, the initial ontology designed to be easily extensible, and related ideas for future work based on this foundation. We foresee this ontology to help also with using various conceptual models to create complete, consistent, and requisite software implementation in an automated way.
Constructing Virtual Radio Center

Radio channels lack visuality and virtual reality platforms are designed to overcome this problem. Virtual objects are very common these days and people get used to virtual platforms. Does this mean people are ready to be involved in such a system? Is this necessary? How to make sounds visible? Use of Virtual Reality is growing rapidly in industry as well as in education area. This chapter is about the virtual radio center construction necessities, usefulness, and its adaptability to real life and to courses.
Multi-level Feature Combination in Dialogue State Tracking

Dialogue State Tracking is one of the most important component in task-oriented dialog system, which can update the dialogue state and accurately estimate the compact state representation. This paper introduces a multi-level feature combination to capture correlation on the basis of the entire dialog and slots. Based on the Memory-Network [], which only consider the relevance of current user utterance with historical dialogue context under turn level, we both consider the correlation at the sentence-level and word-level, and give the slot-value pair as input replacing of candidate state pool to estimate the rare slot-value pair. We use Bi-LSTM and self-attention to capture the correlation between internal words from the utterance, the particular slot-value pair and previous unsettled actions, and finally give a confidence degree score to judge whether the particular slot-value pair is the right answer for the current utterance. Our model achieves a good result on the DSTC-2 dataset about 0.74, which is 3% higher than Memory-Network’s 0.71.
Non-spatial Visualisation

An enormous amount of various data is produced every day. With proper data visualisation, an information hidden in the data can be easily and quickly revealed. It is necessary to create a communication channel that could quickly and efficiently transfer the information from the data to the user. By using visual elements like charts, graphs, and maps, data visualisation is an accessible way to see and understand trends, outliers, and patterns in data. This chapter offers an overview of relevant data visualisations divided into thematic categories and supported by examples.
Preparation of future teachers of musical art for the development of artistic and imaginative thinking of adolescents

The article considers the problem of preparing students of the faculties of arts at pedagogical universities to develop the artistic and imaginative thinking of adolescents. The significance of the development of musical thinking is highlighted: conceptual, logical, emotional-figurative, practical, artistic, eidetic, etc. Artistic-figurative thinking allows you to creatively combine high intelligence with a developed sensual sphere, helps it to evoke bright images and associations in the audience’s listeners music, based on self-experienced experiences. Artistic and imaginative thinking allows you to creatively combine high intelligence with a developed sensual sphere, helping to arouse bright images and associations in the audience of listeners in the process of full-fledged perception of music, based on independently experienced impressions. The article presents several types of imaginative phenomena, and spam: images of perception, imagination, afterimage and eidetic image. In psychological and pedagogical science, there are several types of figurative phenomena, among them: images of perception, imagination, after image and eidetic image. Images of perception – this reflection in the ideal of an external object, which acts on the organs of human senses. Images of imagination – are fictitious, presented in the imagination, but they do not have analogues in reality, and therefore they have never been perceived by sensory organs before. Aidetic imagination is the ability of an individual to see the image of an object, despite its actual absence. At the same time, the thinking image is an accurate reflection of reality, rather than a reconstruction using memory skills. Forward image – a modified, involuntary perception of the ghostly object, which was recently considered in the presence of a strictly immobile view. Eidetic image – a clear, detailed, complete representation of the object, for some time after the termination of its consideration; is different from the image of independence from eye movements and stability over time. The personality of the artistic and musical activity emphasizes the value of the eidetic image, which is characterized by subjective ideas about the object (or a composition of objects) for a certain time after the cessation of current perception, which has clarity and detail. Eidetic images are at an intermediate stage between sensory perception and imagination. It is noted that the specificity of the musical training of future music teachers is characterized by mastery of the basic professional performing skills, a detailed understanding of all the expressive means of music that allow creating a holistic musical and artistic image and bring it to the students.
Reliable frequent itemsets mining with actor-based Apriori algorithm

This paper presents an actor-based Apriori algorithm enhanced with fault tolerance mechanism. All phases of the algorithm including candidate generation and support counting operations are performed by asynchronous actors. When an error occurs during the execution of the algorithm, calculations are interrupted locally for specific actors. The actor state is restored from the snapshot and the operations that caused the failure are either repeated or skipped. Other actors progress with their current tasks. The algorithm can be executed in parallel and distributed environments. Proposed enhancements have been successfully implemented using JAVA and Akka library. This paper discusses the results of the performance of actor-based Apriori algorithm against different datasets. The presented approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.
Camera-based Occlusion Detection for Banknote Sorting

Intelligent technical systems need to become more flexible and adaptive in the context of Cyber-Physical-Systems and Industry 4.0. Cash supply is very important for the worldwide economy. Within the supply chain, handling processes are highly automated, e.g., automated teller machines are installed to ensure an efficient and reliable cash supply. Recently, there is a trend in automated cash handling systems to replace the cash cassettes for each denomination by a simple pitch-in-plastic-bag for all banknotes. This necessitates new automated handling approaches because the bag content will unload irregularly. We propose a new approach which is able to detect the pose and possible occlusions of randomly distributed textured banknotes with simple smartphone cameras. This information can be used to control a handling robot-picking out banknotes.
VR as Library Technology: Early Faculty and Student Feedback on Educational Use of Immersive Technology

In developing a Virtual Reality (VR) project, the library creates a democratic home for a teaching and lab space for a visiting Fulbright scholar to build energy and community around this new immersive technology. Following three academic quarters of VR teaching, workshops, community outreach, and awareness building, interviews of volunteer faculty and student participants provide valuable feedback on their reflection as to the potential of Virtual Reality experiences to scaffold and supplement student learning and their value of the positioning of such technology within the library as a central location. Faculty (N = 5) each provide unique lenses on the importance of the immersive technology within their areas of practice. In addition, two advanced students in the paired Communication Studies VR courses discussed their experiences. Advanced students explained after intensive use of VR software and final project development, the experience changed their thinking about education and future career opportunities. Additional pre-medicine students provided written reflective responses following a VR biology experience (N = 13). Future study is recommended to explore the library as a technology hub and further nuanced details of how immersive technology can expand and scaffold student learning outcomes across a variety of subject areas.
Automatic Vehicle Recognition and Multi-object Feature Extraction Based on Machine Learning

Over the past few years the influence of surveillance has left a massive impact on the social lives of people. The introduction of controlling the surroundings with the surveillance system would provide zero error and faster reaction time. In this proposed work, we have developed a system that will detect and analyze the traffic signal images. Thousands of traffic signal images are fed into the computer and trained based on the margins of particular classes. A weight file is generated from this training process. YOLO (you only look once) is an algorithm used for training the images and detecting the images. It is a network for object detection. In this project, the objects are vehicles and human beings. The identification of objects is done by searching the location on the image and arrange those objects with its prediction level. Existing methods like R-CNN and its variations, used a pipeline methodolgy to analyze and segment the images in multiple steps. Accuracy and speed of recognition is very slow in existing methodologies because of the individually done component training. Proposed methodology with YOLO is performed by a unique neural network. The output describes the computation and name of the classes from a fed image, which is used as vehicles and human beings.
The identifying hidden data features problem solution

Abstract In the article, we considered recommender models based on matrix factorization demonstrate excellent performance in collaborative filtering. The standard Matrix Factorization approach in MLlib deals with clear ratings. To work with implicit data, we used the trainImplicit method. To simulate the processing of real-time data streams, we used the Spark Streaming library, which is responsible for receiving data from the input source and converting the raw data into a discretized stream discretized stream (DStream) consisting of Spark RDD. The rank parameter determines the number of hidden features in the low rank approximation matrices. As a rule, the greater the number of factors, the better, but for a large number of users or elements, it will directly affect the memory usage of the computing system and the amount of data required for training. Therefore, in our problem it was a compromise solution.
Analyzing Big Data Streams with Apache SAMOA

Apache Apache samoa (Scalable Advanced Massive Online Analysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. Apache Apache samoa provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, Apache Samza, and Apache Apex. Apache Apache samoa is written in Java and is available at https://samoa.incubator.apache.org/ under the Apache Software License version 2.0.