Reviewerly/exHarmony huggingface dataset:
Adversarial Deep Factorization for Recommender Systems

Recently, hacking from malicious users is a big challenge for major corporations and government organizations. Tensor factorization methods have been developed to learn predictive models in multi-criteria recommender systems by dealing with the three-dimensional (3D) user-item-criterion ratings. However, they suffer from the data sparsity and contamination issues in real applications. In order to overcome these problems, we propose a general architecture of adversarial deep factorization (ADF) by integrating deep representation learning and tensor factorization, where the side information is embedded to provide an effective compensation for tensor sparsity, and the adversarial learning is adopted to enhance the model robustness. Experimental results on three real-world datasets demonstrate that our ADF schemes outperform state-of-the-art methods on multi-criteria rating predictions. Specifically, the proposed model considers a brilliant combination with adversarial stacked denoising autoencoder (ASDAE), where the adversarial training is used to learn effective latent factors instead of being placed on the extrinsic rating inputs.
Constructional Cyber Physical System: An Integrated Model

Artificial intelligence, machine learning, and deep learning have achieved great success in the fields of computer vision and natural language processing, and then extended to various fields, such as biology, chemistry, and civil engineering, including big data in the field of logistics. Therefore, many logistics companies move towards the integration of intelligent transportation systems. Only virtual and physical development can support the sustainable development of the logistics industry. This study aims to: 1.) collect timely information from the block chain, 2.) use deep learning to build a customer database so that sales staff in physical stores can grasp customer preferences, and 3.) integrate Generative Adversarial Network analysis and logistics truck delivery route analysis. This study will introduce new logistics technology development and innovative smart service structure, covering front-end Internet of Things sensing, mobile application apps, and back-end massive data analysis platform to promote the self/intelligence of logistics. Artificial intelligence for customer preference analysis is used, and images are automatically distributed through the system to reduce labor costs and increase sales. The proposed method is feasible, and it also achieves the push system of information transmission in transportation. Thus, logistics transportation cost transmission is reduced, thereby intelligently pushing self-promotion in marketing activities.
Article 70 Tasks of the Board

Abstract Article 12 (Transparent information, communication and modalities for the exercise of the rights of the data subject) (see too recital 60); Article 42 (Certification) (see too recital 100); Article 43 (Certification bodies); Article 57 (Tasks of supervisory authorities) (see too recitals 123, 129 and 133); Article 58 (Powers of supervisory authorities) (see too recital 129); Article 63 (Consistency mechanism) (see too recital 135); Article 64 (Opinion of the Board) (see too recital 136); Article 65 (Dispute resolution by the Board); Article 66 (Urgency procedure); Article 68 (European Data Protection Board) (see too recital 139); Article 69 (Independence) (see too recital 139).
Evaluation of Difficulty During Visual Programming Learning Using a Simple Electroencephalograph and Minecraft Educational Edition

There are various difficulties encountered by learners during learning such as good or bad learning content, the difficulty level of the learning content, and the degree of learning proficiency. It is possible to detect these difficulties by measuring the browsing history, editing history, and biological information such as brain waves or eye-tracking information. In this paper, we aim to detect the learning state of each learner by measuring brain wave information during learning of visual programming using the Minecraft educational edition. Furthermore, the detected learning status is statistically correlated using the results of a questionnaire conducted after the experiment. The results show that the assessment of task difficulty by learners is more significantly affected by the experience with Minecraft than by the experience with programming.
Attention-Based Temporal Encoding Network with Background-Independent Motion Mask for Action Recognition

Convolutional neural network (CNN) has been leaping forward in recent years. However, the high dimensionality, rich human dynamic characteristics, and various kinds of background interference increase difficulty for traditional CNNs in capturing complicated motion data in videos. A novel framework named the attention-based temporal encoding network (ATEN) with background-independent motion mask (BIMM) is proposed to achieve video action recognition here. Initially, we introduce one motion segmenting approach on the basis of boundary prior by associating with the minimal geodesic distance inside a weighted graph that is not directed. Then, we propose one dynamic contrast segmenting strategic procedure for segmenting the object that moves within complicated environments. Subsequently, we build the BIMM for enhancing the object that moves based on the suppression of the not relevant background inside the respective frame. Furthermore, we design one long-range attention system inside ATEN, capable of effectively remedying the dependency of sophisticated actions that are not periodic in a long term based on the more automatic focus on the semantical vital frames other than the equal process for overall sampled frames. For this reason, the attention mechanism is capable of suppressing the temporal redundancy and highlighting the discriminative frames. Lastly, the framework is assessed by using HMDB51 and UCF101 datasets. As revealed from the experimentally achieved results, our ATEN with BIMM gains 94.5% and 70.6% accuracy, respectively, which outperforms a number of existing methods on both datasets.
Systematic Review of Plant Pest and Disease Identification Strategies and Techniques in Mobile Apps

Identifying plant diseases has been mainstay in any farming and agricultural activity since time immemorial. The most common method has been visually assessing the plants by experts to identify the diseases of the plants and recommending pesticides and chemicals which can be used to treat diseases in plants. Technologies like mobile apps have brought about advancements to this traditional method of identifying and addressing plant diseases. Although there are now a plethora of mobile apps aiding farmers with addressing the issue of plant diseases, there is a scarcity of studies that explore the strategies that are being employed by these apps. This paper identifies the strategies that are being used by mobile apps in plant disease identification to set a foundation for further studies with regards to this field. This is performed through analyzing mobile apps on both Google Play Store and Apple Store, through the description given by the developers and the use of the installed apps to determine the content offered and how it aids farmers in identifying curbing plant diseases. The findings using the inclusion and exclusion criteria that was set showed that overall, they are a few mobile apps in plant pest and disease identification with 152 apps only examined. The database strategy was identified as the most used strategy by developers with 78 mobile apps and the AI Diagnostics was the second most used strategy with 26 mobile apps from both the Google Play Store and Apple App Store combined.
Pose Identification for Task Recognition in Care Work

In Japan, the number of caregivers is not increasing with the same rate as the aging of the population, resulting in an increased workload per caregiver, which has become a social issue. In response, information and communication technology has been introduced to support caregivers, but the tasks it can support are currently limited. In addition, caregivers are subjected to a variety of events and conditions at the work site, which results in a heavy cognitive burden, even on skilled caregivers. To solve this problem, we develop a system to support caregivers, reduce their burden, and improve work efficiency, by observing caregivers and patients using intelligent space. First, it is necessary to understand the caregiver's tasks in detail. In this paper, we propose a task recognition method based on the recognition of poses from images captured by a camera. Moreover, we investigated alternatives to improve the results of the method. Our experiments indicated that the method can recognize the caregiver's pose with 96.9% accuracy, and we successfully solved the problem of insufficient training data.
Unreadable Code in Novice Developers

Throughout his degree, a developer must face multiple challenges, one of the hardest is to understand unreadable code. This paper trying to identify the cause for the lack of usage of coding standards on students, throughout a survey carried out among first and second levels of degree of the Software Engineering. As well as acknowledging signs that would suggest an individual is prone to develop bad coding habits.
Ultimate precision of direct tomography of wave functions

In contrast to the standard quantum state tomography, the direct tomography seeks the direct access to the complex values of the wave function at particular positions (i.e., the expansion coefficient in a fixed basis). Originally put forward as a special case of weak measurement, it can be extended to arbitrary measurement setup. We generalize the idea of "quantum metrology," where a real-valued phase is estimated, to the estimation of complex-valued phase, and apply it for the direct tomography of the wave function. It turns out that the reformulation can help us easily find the optimal measurements for efficient estimation. We further propose two different measurement schemes that eventually approach the Heisenberg limit. In the first scheme, the ensemble of measured system is duplicated and the replica ensemble is time-reversal transformed before the start of the measurement. In the other method, the pointers are prepared in special entangled states, either GHZ-like maximally entangled state or the symmetric Dicke state. In both methods, the real part of the parameter is estimated with a Ramsey-type interferometry while the imaginary part is estimated by amplitude measurements.
UFMDRA: Uterine Fibroid Medicinal Drugs Review Analysis

Abstract Nowadays, enormous reviews are posted online by the consumers which provide related and required knowledge to the similar consumers. Such information is very much crucial for decision making and hence trendy among the web users. This information is very essential not only for potential consumers to make decisions but also for forecasting success and sustainability in commercial businesses. Online reviews on medicinal drugs are important for patients, medical representatives and medical industries. Reviewing medicinal drugs is challenging as sentiment analysis provides very little opportunity to discuss it. Collecting the reviews for uterine fibroid medicines from websites and analysing is a challenging process. An efficient Uterine Fibroid Medicinal Drugs Review Analysis (UFMDRA) model is developed with a decision tree algorithm which is trained and tested for different split ratios to obtain 100% accuracy. Experimental analysis results show that the proposed model has better classification performance in terms of accuracy compared to other classifiers.
Feasibility Analysis of Multimedia Technology Applied in Musical Drama Teaching Class—Taking the Teaching Practice of the Township Version of “Tang Xianzu” as an Example

In today’s fast-developing society, information technology is widely used in various teaching fields, which has a major impact on education and teaching in all aspects. In the classroom teaching of musical dramas, the application of multimedia technology has greatly broadened the teaching philosophy of musical drama teachers and constantly changed the teaching mode of musical drama teaching classrooms. It also made music teachers have to improve their multimedia technology to adapt to modern teaching facilities. Multi-channel surround sound is an inevitable trend in the development of audio-visual technology and a perfect embodiment of art and technology. Multi-channel surround system is applied to multimedia teaching, which brings students a stronger sense of space, positioning, and layering. The impact of audition can fully stimulate students’ enthusiasm and interest in learning. Based on the mature multimedia teaching technology, this article studies the audio production processing technology related to it in detail. On this basis, the multi-channel surround sound of streaming media is also tracked and analyzed. The difference between the two determines the theoretical and technical gap between the two formats and elaborates on the procedures and steps for surround sound production. This article takes the teaching practice of the township version of “Tang Xianzu” as an example to design a multi-channel surround sound system. Multi-channel surround sound technology was introduced into the multimedia software production of the musical drama teaching class. A leap in the production level of multimedia software. It injects vitality into music quality education and broadens the application range of modern educational technology.
Expert System Using Dempster Shafer Method for Pre-Eclampsia Detection

Abstract Pre-eclampsia is a complication in pregnancy that is often diagnosed by hypertension and proteinuria in the second tri-semester of pregnancy. Pre-eclampsia could cause suffering for mother and fetus and also increase the risk of mother and child death. The main objective of this research is to develop an expert system to identify the risk of pre-eclampsia in pregnant women. The device was designed with 16 inputs in the form of symptoms and risk factors that influence the disease. This system uses the Dempster Shafer method for classification of 2 classes: there is a risk of pre-eclampsia and no risk of pre-eclampsia. The test consists of 2 aspects, namely the performance test and user satisfaction test. The results of the research for the performance test showed that the system accuracy reached 88.18% with sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value were 92.72%, 83.63%, 85%, and 92%. User satisfaction test results show “good” in all aspects.
Applying Strategic Planning in a Distance Undergraduate Course in Information Systems: A Case Study

This work presents the process of developing strategic planning for a remote undergraduate course in information systems at a public Brazilian university. This is a case study in which data were collected using documentary analysis through reports from the institutional academic information system and the expertise of professors of the structuring teaching core of the course. A bibliographic survey was also carried out to better understand the subject in focus. The results show that the planning process needs a renewal for continuous improvement and that many difficulties need to be overcome until a level of maturity is reached where the processes of learning, change management, and organizational development are understood as a fundamental part of academic management. The strategic planning model presented in this paper shows that there is a need to implement an organizational culture that can guarantee the installation and development of a learning and organizational change process capable of conferring academic excellence to an undergraduate course.
Improved Performance of Recommender System Based on Demographic Attributes

In today’s Internet era, human beings depend on the Internet for their daily work. With the increase of volatile growth of binary data and the number of visitors on the Internet, it has created data overload challenges and also increase the access time of information/data or products of interest on the Internet. Numbers of business opportunities are created with the implementation of the Internet and easily reach users. The Internet is employed to discover almost everything and anything meaningful information or product. Its 24 * 7 online service has provided the number of choices to the users due to which users faced the information overload problem. Internet users need some tools to search and fast access to appropriate information or products over the Internet. One such tool is called the recommendation system. Nowadays, major e-commerce Web sites are using RS to provide relevant suggestions to their customers. RS is generally used to suggest the most appropriate products to the customers. The functioning of the RS can be based on several parameters like item popularity on the Web site; characteristics of customers like the geographical location; or past buying behavior of customers. The RS is one of the tools that is used to increase the profit of Web sites and also retain the customers. The book RS is used by the Web sites to recommend the books to the customers on their interest. This paper presents the hybrid book recommendation system based on the collective character of collaborative filtering (CF) and collaborative-based (CB) filtering technique.
Windowing Models for Abstractive Summarization of Long Texts

Neural summarization models have a fixed-size input limitation: if text length surpasses the model’s maximal input length, some document content (possibly summary-relevant) gets truncated. Independently summarizing windows of maximal input size disallows for information flow between windows and leads to incoherent summaries. We propose windowing models for neural abstractive summarization of (arbitrarily) long texts. We extend the sequence-to-sequence model augmented with pointer generator network by (1) allowing the encoder to slide over different windows of the input document and (2) sharing the decoder and retaining its state across different input windows. We explore two windowing variants: Static Windowing precomputes the number of tokens for the decoder to generate from each window (based on training corpus statistics); in Dynamic Windowing the decoder learns to emit a token signaling the shift to the next input window. Empirical results render our models effective in intended use-case: summarizing long texts with relevant content not bound to document beginning.
Ontology based multiobject segmentation and classification in sports videos

The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes.
Discussion on the Feasible Application of “Online” + “Offline” Mixed Learning Models of English in Higher Vocational Colleges

With the development of network information technology, higher vocational education has begun to continuously develop online teaching methods. Through the integration of network resources and technology, rich and diverse teaching models have been developed. English is a basic international knowledge that students must master. The use of online and offline mixed learning models can enhance students' enthusiasm, achieve a high integration between online teaching and traditional classrooms, and give full play to their respective advantages. The article briefly analyzes the online and offline mixed learning models, explores the advantages in college English teaching, and analyzes the applied strategies.
Human-in-the-loop handling of knowledge drift

Abstract We introduce and study knowledge drift (KD), a special form of concept drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, the introduction of a new is-a relation between two concepts might be confused with changes to those individual concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in human-in-the-loop applications like smart personal assistants the user knows what kind of drift occurred recently, if any. Motivated by this observation, we introduce trckd , a novel approach that combines two automated stages—drift detection and adaptation—with a new interactive disambiguation stage in which the user is asked to refine the machine’s understanding of recently detected KD. In addition, trckd implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that, when the structure of the concept hierarchy drifts, a handful of queries to the user are often enough to substantially improve prediction performance on both synthetic and realistic data.
Deep Distributional Temporal Difference Learning for Game Playing

We compare classic scalar temporal difference learning with three new distributional algorithms for playing the game of 5-in-a-row using deep neural networks: distributional temporal difference learning with constant learning rate, and two distributional temporal difference algorithms with adaptive learning rate. All these algorithms are applicable to any two-player deterministic zero sum game and can probably be successfully generalized to other settings. All algorithms in our study performed well and developed strong strategies. The algorithms implementing the adaptive methods learned more quickly in the beginning, but in the long run, they were outperformed by the algorithms using constant learning rate which, without any prior knowledge, learned to play the game at a very high level after 200 000 games of self play.
Security of the Poseidon Hash Function Against Non-Binary Differential and Linear Attacks

In the paper, we construct security estimations of Poseidon hash function against non-binary linear and differential attacks. We adduce the general parameters for the Poseidon hash function that allow using this hash function in recurrent SNARK-proofs based on MNT-4 and MNT-6 triplets. We also analyse how to choose S-boxes for such function for this choice to be optimal from the point of view of the number of constraints and security. We show how many full rounds are sufficient to guarantee security of such hash function against non-binary linear and differential attacks. We also calculate the number of constraints per bit achieved in the proposed realizations and demonstrate a considerable gain as compared to the Pedersen hash function.