Reviewerly/exHarmony huggingface dataset:
Comparison of Methods for Determining Activity from Physical Movements

In this study, the methods which can detect the basic physical movements of a person (downward, upward, sitting, stop, walking, running) from inertial sensor data are evaluated. The performances of classical and current approaches to map IMU data to activity classes were compared. A three-stage study was carried out for this aim: 1) data acquisition; 2) creating training/test sets; 3) construction and classification of network architectures. At the stage of data acquisition, to obtain 6 different physical movements from 10 different people, the accelerometer sensor is placed on the persons. Repetitive movements of persons were recorded. At the second stage, the recorded long-term accelerometer data is divided into packages in the form of short-term windows. The training set of classical approaches was constructed by features extracting from each packet data containing one-dimensional acceleration information. The transformation of one-dimensional signals to a two-dimensional image matrix for the training set of the deep learning-based approaches was performed. In the third stage, ANN, SVM, k-NN and CNN architectures were constructed, and classification process was carried out. As a result of the experimental studies, it was found that the accuracy of IMU-activity mapping was 99% with the ANN method and 95% with the CNN method.
Surrogate Scoring Rules

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher scores in expectation. In this article, we extend such scoring rules to settings in which a principal elicits private probabilistic beliefs but only has access to agents’ reports. We name our solution Surrogate Scoring Rules (SSR). SSR is built on a bias correction step and an error rate estimation procedure for a reference answer defined using agents’ reports. We show that, with a little information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the setting with ground truth. As a by-product, SSR induce dominant uniform strategy truthfulness in reporting. Our method is verified both theoretically and empirically using data collected from real human forecasters.
Multi-aircraft Path Planning Method based on Cooperative Search A-star Algorithm

Compared with the single aircraft system, multiaircraft cooperative system has the advantage in multiple complex tasks. This paper mainly focuses on the modeling of the constraints and studies the search algorithm in the cooperative path planning problem. Firstly, based on the constraint of time, space and navigation, the cooperative strategy, cooperative constraint and the cooperative cost are modeled respectively. Then the cooperative search A-star algorithm which solves the path planning problem including condition of multi-aircraft simultaneous arrival and multi-angle arrival is proposed. Finally, the simulation of cooperative path planning is carried out, and the results show that the algorithm can be used to solve the multi-aircraft path planning problem with complex spacetime constraints.
Estimating the single-photon projection of low-intensity light sources

Estimating the quality of a single-photon source is crucial for its use in quantum technologies. The standard test for semiconductor sources is a value of the second-order correlation function of the emitted field below $1/2$ at zero time-delay. This criterion alone provides no information regarding the amplitude of the single-photon contribution for general quantum states. Addressing this question requires the knowledge of additional observables. We derive an effective second-order correlation function, strongly connected to the Mandel-$Q$ parameter and given in terms of both the second-order correlation and the average photon number, that provides a lower bound on the single-to-multi-photon projection ratio. Using both observables individually allows for lower and upper bounds for the single-photon projection. Comparing the tightness of our bounds with those in the literature, we find that relative bounds may be better described using the average photon number, while absolute bounds for low excitation states are tighter using the vacuum projection. Our results show that estimating the quality of a single-photon source based on additional information is very much dependent on what aspect of the quantum state of light one is interested in.
A novel approach for solving skewed classification problem using cluster based ensemble method

In numerous real-world applications, the class imbalance problem is prevalent. When training samples of one class immensely outnumber samples of the other classes, the traditional machine learning algorithms show bias towards the majority class (a class with more number of samples) lead to significant losses of model performance. Several techniques have been proposed to handle the problem of class imbalance, including data sampling and boosting. In this paper, we present a cluster-based oversampling with boosting algorithm (Cluster+Boost) for learning from imbalanced data. We evaluate the performance of the proposed approach with state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost and SMOTEBoost. We conducted experiments on 22 data sets with various imbalance ratios. The experimental results are promising and provide an alternative approach for improving the performance of the classifier when learned on highly imbalanced data sets.
Chinese news text classification based on attention-based CNN-BiLSTM

With the rapid development of text categorization technology, there are still some problems, such as low classification efficiency, low accuracy and incomplete extraction of text features, in the case of large amount of data and too many categorized attributes. In this paper, a hybrid model of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long-term and Short-term Memory Neural Network) combined with Attention (Attention Mechanism) is used to classify and process long text data. CNN extracts feature information from text, then uses BiLSTM to extract context semantics information, combines Attention to distribute weight of text information, and enters softmax classifier to classify. The experimental results show that the feature extraction of this model is more comprehensive, and the classification effect has been improved to a certain extent.
Pengenalan Dan Pelatihan Sitasi Karya Ilmiah Menggunakan Aplikasi Mendeley

Citation is a crucial element for making a scientific paper. in general, novice writers do plagiarism accidentally. The existence of software that can be used to help writing citations, allowing writers to process reference documents they have, and making groupings based on specific topics/categories. The citation cannot be separated from a scientific paper. Citation becomes very important in writing like scientific papers. Usually, the writers of scientific papers get caught in a mistake because they are not careful in writing a citation. Mendeley is a software that has an Open Source concept and can be used freely by lecturers, researchers, and students to process and organize scientific documents and references. The knowledge of students of the Pharmacy Study Program of STIKes Bhamada Slawi about research citations or scientific works is still limited to manuals for practicum subjects, independent assignments, group assignments, or thesis courses. In making citation, it is necessary to pay attention to the procedure, for example the name of the journal which must be abbreviated or written in full. The target activities of this community service are Pharmacy S1 Study Program. The results of this activity are adding new insights for participants in making scientific writings as well as making it easier to search written literature. It is also hoped that the participants will be able to manage scientific references by utilizing Mendeley so that they will improve the quality and dissemination of research from lecturers, researchers, and students of STIKes Bhakti Mandala Husada Slawi.
Unveiling kernel concurrency in multiresolution filters on GPUs with an image processing DSL

Multiresolution filters, analyzing information at different scales, are crucial for many applications in digital image processing. The different space and time complexity at distinct scales in the unique pyramidal structure poses a challenge as well as an opportunity to implementations on modern accelerators such as GPUs with an increasing number of compute units. In this paper, we exploit the potential of concurrent kernel execution in multiresolution filters. As a major contribution, we present a model-based approach for performance analysis of as well single- as multi-stream implementations, combining both application- and architecture-specific knowledge. As a second contribution, the involved transformations and code generators using CUDA streams on Nvidia GPUs have been integrated into a compiler-based approach using an image processing DSL called Hipacc. We then apply our approach to evaluate and compare the achieved performance for four real-world applications on three GPUs. The results show that our method can achieve a geometric mean speedup of up to 2.5 over the original Hipacc implementation without our approach, up to 2.0 over the other state-of-the-art DSL Halide, and up to 1.3 over the recently released programming model CUDA Graph from Nvidia.
Formation of Motivation for Professional Communication among Future Specialists of Pedagogical Education

The article deals with the problem of qualitative training of a future specialist under the reforming of the educational process content.The peculiarities of the diagnostics process of level of motivation formation to professional communication are considered.It is identified that the preparation of the future specialist of pedagogical education to professional activity is the process of formation of a complex of motives, knowledge, skills and abilities, personal qualities, which give him a real opportunity of professional and personal development, detecting his creative activity; movement from the standpoint of imitation (reproductive level) to the position of the creator of his professional life, himself as a creative person (creative level), co-creator of the educational process (level of creative collaboration).The importance of the formation of motivation for the professional development of the future specialist has been clarified.The authors examine the diagnostics of initial level of formation of motivation to professional communication as the important factor of future specialists' creativity formation.The authors describe in details the experimental work, which is based on priority principles of targeting performance and voluntariness.The essence of the components in the formation of creative activity of future pedagogues has been revealed.It has been concluded that the developed model of motivation of future pedagogues contributes to the increase of the level of formation of readiness to professional communication.
A user-centered Perspective on Interactive Data Visualization. A digital flâneries into the documentation of the Historical Italian Mind Science Archive.

The data visualization debate is often polarized between the two dominant position of communication design and data science.A human-centered approach can introduce a third perspective putting people who'll use the information in a prominent role in the design process.According to user research methods, user personas and task-based testing, and co-design activities, the paper presents a case study developed for the Historical Italian Mind Science Archive (Aspi).The project has been focused on the experience of the users when surfing the hierarchical structure of the Aspi website looking for biographical information about the protagonists or searching for primary and original documents -such as correspondence, writings, notes -inside the digitized archives and their inventory.Interactive data visualization, on one hand, and human-centered design, on the other, are the two drivers chosen to make links and connections -otherwise embedded in the historical knowledge of experts in the field -explicit or, better to say, visible.The results are different conceptual and visual displays of the archives and curated contents of the website offering dynamic and interactive insights according to three different selected criteria: time, space, people.Activities of expert walk-through and user-testing, used in the research phase of the process have been used to evaluate the final results to understand and assess the overall experience of users and to validate the initial design hypothesis to extend the approach beyond the proposed case study.
Inter-Campus Sharable Potential of Hardware Educational Resources in A University Town: Connotation, Determination Method and A Case Study

Sharing educational resources (ERs) among campuses in a university town (UT) could avoid the waste or repeated construction of ERs, which is beneficial to the sustainable utilization of resources. For hardware educational resources (HERs) greatly attached to land resources, whether they have surplus shareable potential is the key factor relating to inter-campus sharing. However, there is a serious lack of definition and quantitative research on HERs’ shareable potential. Based on the correlation between land resource utilization and HER sharing, we firstly defined the connotation of inter-campus sharable potential of HERs (ISPHERs), put forward its calculation method and carried out a case study in Longzi Lake UT of Zhengzhou City, Henan province, China. The results show that: (1) ISPHERs could be defined from the perspective of “the best utilization degree” which mainly depended on the utilization and development gap between the present area value and the possible most intensive one under certain conditions; (2) based on the area quota and the students’ number, the ISPHERs was calculated by the Delphi and multi-factor model methods; (3) the case study in Longzi Lake UT of Zhengzhou City, Henan province, China showed that the comprehensive ISPHERs was high in the south and low in the north, and the libraries and canteens in five colleges and universities mainly providing vocational skills training were insufficient. If all the 11 campuses could freely share HERs, there would be an overall surplus of the five kinds of HER (i.e., classroom, laboratory, library, indoor sports room and canteen). The comprehensive management framework should include sustainable concept guidance, cooperation system construction, planning control, benefit adjustment and technological innovation, etc.
Are You Going to Answer That? Measuring User Responses to Anti-Robocall Application Indicators

Robocalls are inundating phone users.These automated calls allow for attackers to reach massive audiences with scams ranging from credential hijacking to unnecessary IT support in a largely untraceable fashion.In response, many applications have been developed to alert mobile phone users of incoming robocalls.However, how well these applications communicate risk with their users is not well understood.In this paper, we identify common real-time security indicators used in the most popular anti-robocall applications.Using focus groups and user testing, we first identify which of these indicators most effectively alert users of danger.We then demonstrate that the most powerful indicators can reduce the likelihood that users will answer such calls by as much as 43%.Unfortunately, our evaluation also shows that attackers can eliminate the gains provided by such indicators using a small amount of targetspecific information (e.g., a known phone number).In so doing, we demonstrate that anti-robocall indicators could benefit from significantly increased attention from the research community.
Efficient Outlier Detection for High Dimensional Data using Improved Monarch Butterfly Optimization and Mutual Nearest Neighbors Algorithm: IMBO-MNN

The hybrid Improved monarch butterfly optimization-mutual nearest neighbor (IMBO-MNN) is proposed for outlier detection in high dimensional data.It is a challenge to detect outliers in high dimensional information.The external behavior of the data points cannot be detected in high-dimensional data except in the locally relevant data sub-sets.Subsets of dimensions are called subspaces, and with an increase in data dimension, the number of those subspaces grows exponentially.In another subspace an information point that is an outlier can appear ordinary.It's essential to assess its outlier behavior according to the amount of subspaces in which it appears as an outermost part to characterize an outlier.Data is scarce in high-dimensional space and the concept of closeness does not preserve meaning.In fact, the sparsity of the high-dimensional data means that every point is nearly equal from the point of view of closeness-based finishes.As a result, for higher dimensional information finding is more complicated and non-obviously significant outliers.An enhanced MBO (IMBO) algorithm is offered for enhanced search precision and run time efficiency by a fresh adaptation provider.Statistical results indicate that the elevated local optimal prevention and quick convergence rate of the improved monarch butterfly optimization (IMBO) algorithm helps to exceed the basic MBOs in outlier detection.Comparatively, IMBO produces very competitive outcomes and tends to surpass present algorithms.Optimal value k remains a task, affecting the efficiency of kNN straightforwardly.We are presenting a fresh learning algorithm under kNN in this paper to alleviate this issue called mutual nearest neighbor (MNN).The main feature of our method is that the class marks of unknown instances are defined by mutually next to one another, instead of by closest neighbor.The advantage of mutual neighbors is that in the course of the prediction process pseudo close neighbors can be identified and taken not into account.The performance of the suggested algorithm has been examined with a number of studies.For 100 data, IMBO-MNN is in 4897 milliseconds, PSO is in 5239 milliseconds, random forest is 5347 milliseconds, PNN is 5278 milliseconds and KNN is in 5166 milliseconds.For 250 data, IMBO-MNN is in 5984 milliseconds, PSO is in 6132milliseconds, random forest is 6145 milliseconds, PNN is 6124 milliseconds and KNN is in 6152 milliseconds.For 500 data, IMBO-MNN is in 6416 milliseconds, PSO is in 6636 milliseconds, random forest is 6634 milliseconds, PNN is 6719 milliseconds and KNN is in 6710 milliseconds.For 1000 data, IMBO-MNN is in 6913 milliseconds, PSO is in 7111 milliseconds, random forest is 7019 milliseconds, PNN is 7134 milliseconds and KNN is in 7162 milliseconds.The proposed IMBO-MNN performs better with minimum time taken.The findings indicate that the technique can identify outliers in high dimensional data efficiently in a decreased calculation moment.
Evaluating the Robustness of Defense Mechanisms based on AutoEncoder Reconstructions against Carlini-Wagner Adversarial Attacks

Adversarial Examples represent a serious problem affecting the security of machine learning systems. In this paper we focus on a defense mechanism based on reconstructing images before classification using an autoencoder. We experiment on several types of autoencoders and evaluate the impact of strategies such as injecting noise in the input during training and in the latent space at inference time.We tested the models on adversarial examples generated with the Carlini-Wagner attack, in a white-box scenario and on the stacked system composed by the autoencoder and the classifier.
Machine learning for buildings’ characterization and power-law recovery of urban metrics

In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings’ number of floors and construction periods’ dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow.
Development and Integration of E-learning Services Using REST APIs

E-Learning systems have gained a lot of traction amongst students and academicians due to their flexible nature in terms of location independence, time, effort, cost and other resources. The rapidly changing nature of the education domain makes the design, development, testing, and maintenance of E-Learning systems complex and expensive. In order to adapt to the changing policies of educational institutes as well as improve the performance of students, the paper presents a Service-Oriented Architecture (SOA) approach to minimize the cost and time associated with the development of E-Learning systems. The paper illustrates the development of independent E-Learning web services and how they can be combined to implement the required policies of respective education institutes. The paper also presents a sample policy implemented using developed web services to achieve the required objectives.
Analysing picking errors in vision picking systems

Abstract Vision picking empowers users with access to real-time digital order information, while freeing them from handheld radio frequency devices. The smart glasses, as an example of vision picking enabler, provide visual and voice cues to guide order pickers. The glasses mostly also have installed navigation features that can sense the order picker’s position in the warehouse. This paper explores picking errors in vision systems with literature review and experimental work in laboratory environment. The results show the effectiveness of applying vision picking systems for the purposes of active error prevention, when they are compared to established methods, such as paper-picking and using cart mounted displays. A serious competitor to vision picking systems are pick-to-light systems. The strong advantage of vision picking system is that most of the errors are detected early in the process and not at the customer’s site. The cost of fixing the error is thus minimal. Most errors consequently directly influence order picker’s productivity in negative sense. Nonetheless, the distinctive feature of the system is extremely efficient error detection.
Sustainable motivation for teaching as a component of teacher education students’ professional flexibility

УДК 378+159