Reviewerly/exHarmony huggingface dataset:
Self-Assesment tool with topic-driven navigation for algorithms learning

Algorithms and data structures are one of the most difficult parts of Computer Science for students to learn. It is therefore essential to develop approaches and tools that facilitate the acquisition of this crucial part of a computer scientist&#x2019;s training. Based on the hypothesis that making the access to information and self-assessment easier improves the learning of the concepts, in this work we propose a graphical tool that allows the student to navigate through the topics of the subject, and to reach collections of self-assessment exercises on each of the topic. For this purpose, the information of the subject has been organized in a navigable hierarchy of topics that allows students to access exercises in a simple and visual way, on the one hand, but also to easily learn the relationships between the different concepts of the subject. These exercises allow students to self-evaluate and solve their doubts, as well as to find analogies between different topics in the hierarchy. This process is complemented by a web page recommender for the topics of the hierarchy. Results according to a questionnaire about the tool answered by the students indicate that it is considered of high utility and interest.
We Can Define the Domain of Information Online and Thus Globally Uniformly

Any information is (transported as) a selection from an ordered set, which is the “domain” of the information. For example, any piece of digital information is a number sequence that represents such a selection. Its senders and receivers (with software) should know the format and domain of the number sequence in a uniform way worldwide. So far, this is not guaranteed. However, it can be guaranteed after the introduction of the new “Domain Vector” (DV) data structure: “UL plus number sequence”. Thereby “UL” is a “Uniform Locator”, which is an efficient global pointer to the machine-readable online definition of the number sequence. The online definition can be adapted to the application so that the DV represents the application-specific, reproducible features in a precise (one-to-one), comparable, and globally searchable manner. The systematic, nestable online definition of domains of digital information (number sequences) and the globally defined DV data structure have great technical potential and are recommended as a central focus of future computer science.
Swellfish privacy: Supporting time-dependent relevance for continuous differential privacy

Today, continuous publishing of differentially private query results is the de-facto standard. However, even today’s most advanced privacy frameworks for streams are not customizable enough to consider that privacy goals of humans change as quickly as human life. We name this time-dependent relevance of privacy goals. Instead, upon design time, one needs to estimate the worst case. Then, one hopes that this protection is sufficient and accepts that one protects against this case all the time, even if it is currently not relevant. Designing a privacy framework being aware of time-dependent relevance implies two effects, which – properly exploited – allow to tune data utility beyond incremental design of a novel privacy mechanism for an existing framework. In this paper, we propose such a new framework, named Swellfish Privacy. We also introduce two tools for designing Swellfish-private mechanisms, namely, time-variant sensitivity and a composition theorem, each implying one effect a mechanism can exploit for improving data utility. In a realistic case study, we show that exploiting both effects improves data utility by one to three orders of magnitude compared to state-of-the-art w-event DP mechanisms. Finally, we generalize the case study by showing how to estimate the strength of the effects for arbitrary use cases.
Passive Target Tracking in Underwater Environment using Bearing and Frequency Measurements

Marine resources may now be explored because of advancements in acoustic technology and instruments that were previously unavailable. Underwater acoustic target tracking has gotten a lot of attention nowadays, both in the military and in the civilian world, because it is such an important component of ocean exploration. Due to the complex nature of the marine environment, several tracking approaches have been proposed to improve tracking performance in the maritime environment. The Doppler Bearing tracking (DoBT) of an undersea target can be used to safeguard maritime territory while also allowing for the efficient use of available marine resources. When taking into account the impacts of an unknown underwater environment, the goal of this study was to estimate the 2-D positions and velocities of an unknown underwater target (i.e., Target Motion Parameters (TMP)) with undetermined underwater disturbances. In this research paper, the UKF algorithm is compared against CRLB to demonstrate its accuracy.
Tematyka piłkarska w kształtowaniu kompetencji interkulturowej studentów kierunków filologicznych na przykładzie filologii włoskiej

The aim of the present article is to offer some reflections on the possibilities of developing intercultural competence in the context of glottodidactics by employing some elements constituting the language of football and sport. It is demonstrated that such elements as football-related lexems, anthroponyms of Italian football players, Italian football and fascism, cultural scripts, and many others can be used to shape students’ awareness as regards Italian culture, society, lifestyle, and customs. Consequently, one facilitates the enculturation process and the achievement of intercultural competence.
Research on image recognition method based on LMAL and VGG-16

In deep convolutional neural networks, the traditional Softmax loss function lacks the ability to distinguish similar classes. In order to solve this problem, the idea of increasing the inter-class spacing and reducing the intra-class spacing is widely recognized. The large margin angular loss (LMAL) loss function is introduced to reduce the intra-class spacing by L2-standardization of the features and weight vectors of the Softmax loss function. At the same time, LMAL also has a good ability to distinguish deeper features. Combined the LMAL loss function and the VGG-16 model, the results on three independent datasets show that the image recognition accuracy of the improved model has been significantly improved.
Cost-forced and repeated selective information minimization and maximization for multi-layered neural networks1

The present paper aims to propose a new information-theoretic method to minimize and maximize selective information repeatedly. In particular, we try to solve the incomplete information control problem, where information cannot be fully controlled due to the existence of many contradictory factors inside. For this problem, the cost in terms of the sum of absolute connection weights is introduced for neural networks to increase and decrease information against contradictory forces in learning, such as error minimization. Thus, this method is called a “cost-forced” approach to control information. The method is contrary to the conventional regularization approach, where the cost has been used passively or negatively. The present method tries to use the cost positively, meaning that the cost can be augmented if necessary. The method was applied to an artificial and symmetric data set. In the symmetric data set, we tried to show that the symmetric property of the data set could be obtained by appropriately controlling information. In the second data set, that of residents in a nursing home, obtained by the complicated procedures of natural language processing, the experimental results confirmed that the present method could control selective information to extract non-linear relations as well as linear ones in increasing interpretation and generalization performance.
Privacy-friendly Discovery of Common Friends in P2P Networks

In this paper we study the problem of comparing a set of data between two parties in a peer-to-peer network to determine the number of common friends. Several protocols for private set intersection are presented in the literature. When the sets are large these tend to be too slow for many purposes. We consider the problem of two parties finding out how many common friends they have in a privacy preserving way. This problem has arisen in designing a peer-to-peer platform called HELIOS. We present our solution for the problem that is more efficient than older protocols but still sufficiently privacy-friendly for our purposes. The solution is based on iteratively revealing information about the hash values of friends’ identities in small increments.
Patient History Tracking using Local Binary Pattern Histogram(LBPH) Algorithm

Currently hospitals maintain the patient details either manually or digitally. History of patients like disease caused, treatment undergone earlier, current status and details of each and every review are maintained for future reference. But in emergencies retrieving information of patients leads to waste of time and even this searching process delays remedial treatment and causes uncertain death to patients. Since in health care time is very critical, identifying patients and tracking patient details in emergencies can be done using efficient mechanisms. Even though existing systems use plenty of security identification solutions like patient-id, biometric methods like fingerprint and two factor authentication none of the method is as strong as facial recognition. This may also reduce the medical errors while identifying patients even if the patient is in unconscious state and to track the information and to reduce the time taken at each counter. Thus by integrating health care with Technology lots of solutions are given since technology can ease the heavy workload, the way the disease can be diagnosed and treated. Our proposed method will provide an efficient technique for patient identification and tracking the patient's history. Though the proposed method is based on a math pattern, it will keep the data safe and sound. The system will correctly identify if a person is allowed in certain floors or other restricted areas, or if they move out of their pre-defined territory. This method is suitable for nursing homes or Supporting Living Facilities to keep the patients in safe condition. By adding cameras along with facial recognition algorithms in patient rooms used to verify who was it would be easy to identify who were in the room before recording patient health information on video screens. Thus the proposed method uses facial recognition to track the patient history even if they are in unconscious state by using Local Binary Pattern Histogram (LBPH) algorithm. This algorithm describes original image in an enriched form by enhancing facial features obtained from intermediate images.. Thus by using the proposed method the time taken to identify patients and tracking their records can be reduced and so it can be used to revolutionize the way the patients are treated in the hospitals.
Perceived Teacher Affective Support and Cognitive and Psychological Engagement among STEM Students in Distance Learning

Teacher affective support and student engagement are both fundamental to making the educative process meaningful in distance learning environments. This study assessed the levels of perceived teacher affective support and cognitive and psychological engagements among Science, Technology, Engineering, and Mathematics (STEM) students enrolled in online, modular, and blended distance learning modalities. The study also examined the relationship between the respondents’ perceived teacher affective support and cognitive and psychological engagement. Descriptive research design and correlational method were used in the study. A total of 401 STEM students from a public senior high school in the Santa Cruz district, Province of Laguna participated in the study. Two self-report instruments were used and administered in the study. Results showed that respondents’ perceived teacher affective support was to a very great extent. Cognitive engagement among the respondents was similar to a very great extent, while data showed that participants’ psychological engagement was at a great extent. Pearson’s test of correlation revealed that the respondents’ perceived teacher affective support is significantly correlated to cognitive engagement. Likewise, the study also demonstrated a significant relationship between perceived teacher affective support and psychological engagement. The data indicate that teacher affective behaviors correlate with student engagement in distance learning experiences. The findings of the study may provide a framework for educators and administrators in flexible learning modalities and alternative delivery modes.
Photovoltaic power output forecasting based on similar day analysis and sensitive pruning extreme learning machine

A prediction model (SDA-SP-ELM) based on the combination of similar day analysis (SDA) and sensitive pruning extreme learning machine (SP-ELM) is proposed to predict the hourly output power of PV plants for the problem of low accuracy of PV output prediction. Firstly, samples with similarities to the prediction date are selected as model training data by the Pearson coefficient method, avoiding the input of redundant information. Secondly, the optimal number of nodes in the hidden layer is determined from the use of a sensitive pruning extreme learning machine, which greatly improves the generalization ability of the model and avoids the problem of increasing the complexity of the model. And the simulation results show that the proposed model can accurately predict the hourly PV plant output power, and it is significantly better than SDA-ELM and similar day analysis back-propagation neural network (SDA-BPNN) in terms of accuracy and stability of PV output prediction, with a difference of about 1.2516 kW in MAE value and 85.99% in nRMSE error compared to SDA-ELM.
Implementation of a Human Motion Capture System Based on the Internet of Things Machine Vision

The classification of the stereo matching comprehensive analysis related algorithm model can be subdivided into local stereo matching based on the entire acquisition and global stereo matching based on the entire local. But it can have a higher capture efficiency because the log-likelihood variance cost calculation function can have a faster feature convergence capture speed than the ordinary log-mean-square error cost function. Through the combination of gray channel and frame difference channel, a better network structure and parameters on the KTH data set are obtained, which can ensure the classification effect while greatly reducing the number of parameters, improving training efficiency and improving classification accuracy. The article uses dual-channel 3D convolutional human neural network technology to achieve 92.5% accuracy of human feature capture, which is significantly better than many traditional feature extraction techniques proposed in the literature.
Conceptual semantic enhanced representation learning for event recognition in still images

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Relations between the average bipartite entanglement and N-partite correlation functions

Abstract We study the relations between the average bipartite entanglement and the N-partite length of correlation. We show that the N-partite length of correlation can completely determine the average bipartite entanglement for two and three-qubits. For four-, five- and six-qubit systems, the N-partite correlation functions may not determine the average bipartite entanglement. These results are novel and promising for pure state.
On definability of team relations with k-invariant atoms

We study the expressive power of logics whose truth is defined over sets of assignments, called teams, instead of single assignments. Given a team X, any k-tuple of variables in the domain of X defines a corresponding k-ary team relation. Thus the expressive power of a logic L with team semantics amounts to the set of properties of team relations which L-formulas can define. We introduce a concept of k-invariance which is a natural semantic restriction on any atomic formulae with team semantics. Then we develop a novel proof method to show that, if L is an extension of FO with any k-invariant atoms, then there are such properties of (k+1)-ary team relations which cannot be defined in L. This method can be applied e.g. for arity fragments of various logics with team semantics to prove undefinability results. In particular, we make some interesting observations on the definability of binary team relations with unary inclusion-exclusion logic.
Double Linear Transformer for Background Music Generation from Videos

Many music generation research works have achieved effective performance, while rarely combining music with given videos. We propose a model with two linear Transformers to generate background music according to a given video. To enhance the melodic quality of the generated music, we firstly input note-related and rhythm-related music features separately into each Transformer network. In particular, we pay attention to the connection and the independence of music features. Then, in order to generate the music that matches the given video, the current state-of-the-art cross-modal inference method is set up to establish the relationship between visual mode and sound mode. Subjective and objective experiment indicate that the generated background music matches the video well and is also melodious.
Analysis of Customer Review and Predicting Future Release of the Product using machine learning concepts

The customer reviews are in unstructured form and natural language. To make data structured, natural language processing algorithms like sentimental analysis are used. This method is to extract whether the reviews are positive, negative, or neutral in the state. Sentimental analysis is used to capture each product's opinions and feelings about the particular product. The main objective of the proposed work is to predict the future release of the product. For prediction, machine learning algorithms along with sentimental analysis are added that provide better performance. In the proposed work, firstly the data is collected, and then it is preprocessed. Secondly, Vader sentiment analysis is implemented for analyzing the customer reviews followed by extracting the features. Random forest classifiers were carried out for improving the performance pursued by predicting the future release of the product using a decision tree algorithm. The proposed work provides and improves performance results compared to the existing works.
Smarter Education Methodologies for MET: An approach with Augmented Reality

The current Instructional Designs of Maritime Education and Training (MET) relies on models, oriented towards competency enhancement in a vocational backdrop. While outcome based models are being implemented to improve the formats, experiential learning methods have been proposed. These include simulator based training, virtual reality modules etc., which attempt to bring the physical and behavioural realism. It is deliberated that: inclusion of Augmented Reality (AR) in the formats would make training more effective.In this study, a pilot approach to investigate the effectiveness of AR has been followed. Conceptual Training Model based on AR is demonstrated to a selected target student population from Marine engineering discipline. They were put under this experience and the effects tested with formative and summative assessments. The variables for testing the hypothetical case on improvements are then formulated and probability of analysing impact of A R methodology on learner’s cognitive load and psychomotor skills will be proposed. This paper is limited to the review of literature and proposals of few AR models in the Competency formats of marine engineering training.
Analysis of Accuracy Rate of Distributed Virtual Reality Face Recognition Based on Personal Intelligent Terminal

Due to the fact that problem occurs to the traditional algorithm in the internal change process of the sample set, this paper proposes a distributed virtual reality face recognition algorithm based on the personal intelligent terminal. By the utilization of the face image library with multiple description functions through expansion, the pixel intensity of the face image has data information, the original face image can be used to generate a face image with higher intensity pixels, and the mirror image can increase the detailed data information of the image. By effectively combining source domain images with initialization images, a scalable image set can be generated. According to parameter-free modeling, the image can be extended as a personal intelligent terminal. The test sample images of the same category and different source domains can form an image set and finally use the residual discriminant function to complete the face recognition. Finally, the results of the experimental analysis show that the distributed virtual reality face recognition accuracy analysis method proposed in this paper can not only build a face image database with multiple reconstruction functions but also effectively use the correlation within the face samples to improve the accuracy of face recognition and implement face recognition. Compared with other facial recognition algorithms, this algorithm has higher recognition accuracy and faster recognition speed.
A Survey of Explainable Artificial Intelligence in Bio-signals Analysis

Background: In contrast to the high rate of interest in artificial intelligence (AI) for business, the rate of AI adoption is much lower. It has been found that lack of consumer trust would adversely influence consumer’s evaluations of information given by AI. Hence the need for explanations in model results. MethodS: This is especially the case in clinical practice and juridical enforcement where improvements in prediction and interpretation are crucial. Bio-signals analysis such as EEG diagnosis usually involves complex learning models, which are difficult to explain. Therefore, the explanatory module is imperative if results is to be released to the general public. This research shows a systematic review of explainable artificial intelligence (XAI) advancement in the research community. Recent XAI efforts on bio-signals analysis were reviewed. The explanatory models are found to be in favor compared to the interpretable model approach due to the popularity of deep learning models in many use cases. Result: The verification and validation of explanatory models appear to be one of the crucial gaps in XAI bio-signals research. Currently, human expert evaluation is the easiest validation approach. Although the human directed approach is highly trusted by the bio-signals community, but it suffers from persona and social bias issues. Conclusion: Hence, future research should investigate on more objective evaluation measurements towards achieving the characteristics of inclusiveness, reliability, transparency, and consistency in XAI framework.