Reviewerly/exHarmony huggingface dataset:
Safe object initialization, abstractly

Objects under initialization are fragile: some of their fields are not yet initialized. Consequently, accessing those uninitialized fields directly or indirectly may result in program crashes or abnormal behaviors at runtime.
Architectural Access Control Policy Refinement and Verification under Uncertainty

In our connected world, confidentiality is a central quality requirement. A commonly used mechanism to meet confidentiality requirements is access control. However, access control policies are usually not defined on the architectural abstraction level and are imprecise during design time due to the high degree of uncertainty. This impedes early considerations of confidentiality as implied by Privacy by Design. We propose an approach to refine and verify access control policies while handling uncertainty that fills the gap between high-level confidentiality requirements and low-level access control.
Expert System for Diagnosing Cocoa Diseases Using the Dempster Shafer Method

Indonesia occupies the third position of the largest cocoa producing country in the world, but the challenge faced is that the disease that attacks the cocoa plant causes only 60% of the cocoa production is export-worthy. The Dempster Shafer method can be used to diagnose types of diseases in cocoa plants. The system can be used by cocoa farmers in Indonesia to minimize the attack of cocoa plant diseases, so that yields can be abundant. This research was conducted at P4S Karya insani, Linking. As a research location for 6 months, from September 2018 to March 2019, it includes taking data, analysis, develop the applications, implementation, and testing. Expert system diagnosis of cocoa plant diseases using the dempster shafer method makes it easy for users to know the symptoms and diseases of the cocoa plant so that it can be used in diagnosing cocoa plant diseases.
Gymnastics Movement Signs Based on Network Communication and Body Contour Feature Extraction

With the rapid development of computer vision technology, human action recognition technology has occupied an important position in this field. The basic human action recognition system is mainly composed of three parts: moving target detection, feature extraction, and human action recognition. In order to understand the action signs of gymnastics, this article uses network communication and contour feature extraction to extract different morphological features during gymnastics. Then, the finite difference algorithm of edge curvature is used to classify different gymnastic actions and analyze and discuss the Gaussian background. A modular method, an improved hybrid Gaussian modeling method, is proposed, which adaptively selects the number of Gaussian distributions. The research results show that, compared with traditional contour extraction, the resolution of gymnastic motion features extracted through network communication and body contour features is clearer, and the increase rate is more than 30%. Moreover, the method proposed in this paper removes noise in the image extraction process, the effect is good, and the athlete’s action marks are very clear, which can achieve the research goal.
GDPR Compliance IoT Authentication Model for Smart Home Environment

The Internet of things (IoT) became quickly one of the most popular and most discussed topics in research. Studies paid attention to the Internet stuff, primarily to new products that aim to achieve greater efficiency and simplicity in life. IoT may cover several fields of the smart environment. Because of the data exposure that occurs when data is transferred via various channels, data protection issues have become a major problem as the company continues to expand. When user privacy and property are taken into consideration, the situation may become much worse. As a result, the authentication process for communicating entities has garnered considerable attention. In this paper, we proposed a secure authentication model for smart home applications, which privacy considered and complies with the General Data Protection Regulation GDPR. The proposed scheme improved the existing authentication schemes' performance and security level. This work based on the Elliptic curve cryptography ECC, one-way hash function, and XOR operation. The proposed lightweight authentication model is suitable for resource-constrained devices. This study is developing the offline direct authentication model to authenticate users and IoT devices in the local network. In addition, our scheme uses the online authentication server to authenticate all system parts.
Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA

Inorganic contaminants, including potentially toxic metals (PTMs), originating from un-reclaimed abandoned mine areas may accumulate in soils and present significant distress to environmental and public health. The ability to generate realistic spatial distribution models of such contamination is important for risk assessment and remedial planning of sites where this has occurred. This study evaluated the prediction accuracy of optimized ordinary kriging compared to spatial regression-informed cokriging for PTMs (Zn, Mn, Cu, Pb, and Cd) in soils near abandoned mines in Bumpus Cove, Tennessee, USA. Cokriging variables and neighborhood sizes were systematically selected from prior statistical analyses based on the association with PTM transport and soil physico-chemical properties (soil texture, moisture content, bulk density, pH, cation exchange capacity (CEC), and total organic carbon (TOC)). A log transform was applied to fit the frequency histograms to a normal distribution. Superior models were chosen based on six diagnostics (ME, RMS, MES, RMSS, ASE, and ASE-RMS), which produced mixed results. Cokriging models were preferred for Mn, Zn, Cu, and Cd, whereas ordinary kriging yielded better model results for Pb. This study determined that the preliminary process of developing spatial regression models, thus enabling the selection of contributing soil properties, can improve the interpolation accuracy of PTMs in abandoned mine sites.
Decision Support System for Assessment of Learning Process Using Simple Additive Weighting

Lecturers are entitled to get promotions and awards in accordance with their academic performance. Service of teaching and learning process of quality and professional will affect hope and feeling of satisfaction for student to spirit in follow lecture. Performance appraisal according to the favorite lecturer selection guidelines are (a) giving the syllabus at the beginning of the semester, (b) teaching on time, (c) mastering the taught material, (d) correcting assignments and timely grades and (e) serving academic guidance students well. Lecturers who can be elected are a lecturer who have given lectures on all courses in the department at least two semesters and are not currently following the learning task and has provided academic guidance services at least two semesters. Because there is no objective method to decide better and faster, based on existing data. Making the information system that can provide recommendations needed a proper method of decision making. Simple Additive Weighting (SAW) method is appropriate for the decision-making process because it can determine the weight value for each attribute, then proceed to the ranking process that will select the best alternative from some of the best alternatives.
Recycling Discriminator

Generative adversarial networks (GANs) have been extensively used for training networks that perform image generation. After training, the discriminator in GAN was not used anymore. We propose to recycle the trained discriminator for another use: no-reference image quality assessment (NR-IQA). We are motivated by twofold facts. First, in Wasserstein GAN (WGAN), the discriminator is designed to calculate the distance between the distribution of generated images and that of real images; thus, the trained discriminator may encode the distribution of real-world images. Second, NR-IQA often needs to leverage the distribution of real-world images for assessing image quality. We then conjecture that using the trained discriminator for NR-IQA may help get rid of any human-labeled quality opinion scores and lead to a new opinion-unaware (OU) method. To validate our conjecture, we start from a restricted NR-IQA problem, that is IQA for artificially super-resolved images. We train super-resolution (SR) WGAN with two kinds of discriminators: one is to directly evaluate the entire image, and the other is to work on small patches. For the latter kind, we obtain patch-wise quality scores, and then have the flexibility to fuse the scores, e.g., by weighted average. Moreover, we directly extend the trained discriminators for authentically distorted images that have different kinds of distortions. Our experimental results demonstrate that the proposed method is comparable to the state-of-the-art OU NR-IQA methods on SR images and is even better than them on authentically distorted images. Our method provides a better interpretable approach to NR-IQA. Our code and models are available at https://github.com/YunanZhu/RecycleD.
Website-Based Agricultural Seed Information System In The Sumedang Area

Agriculture is an economic sector that is very important in the national economy. National economic development in the 21st century will still be based on agriculture. The stages that are in line with economic development, agriculture-based services and business activities will increase, namely agribusiness activities will become one of the leading activities of national economic development in a wide variety of aspects. The software development method used is the Prototype method, which plays an important role in managing the system. While the method used for system testing is the User Experience Questionnaire. This research has produced a web-based information system that can display agricultural seeds which are expected to help farmers determine which seeds to plant
The Development of Physical Activity Leaders Using a Contemplative Education Approach to Promote Wellness among the Elderly

The purpose of this study was to develop physical activity leaders using a contemplative education approach to promote wellness among the elderly. This study used the qualitative method design. All subjects were 30 physical education and exercise science students. The instrument focus on the contemplative approach with the 7Cs, which are Contemplation, Compassion, Connection, Confronting, Continuity, Commitment, and Community. The results from reflective papers, observation and focus group were analysed by content analysis. The result of this study shows that the students learned to become the leader leading the elderly to do physical activity. The outcome of the learning came in three domains. The first one was cognitive domains. It was found that the learners had understood the lessons in the course and could connect them with self-awareness, recognizing the differences between the elderly's physical body and mind. Secondly, the psychomotor domain was presented as the students had learnt to focus. All these are learned through contemplation and acknowledging the importance of deep conversation. Thus, they made possible physical activity leaders who could promote health for the elderly. Lastly, the affective domain shows that the learners had transformed their abilities into a fundamental domain. They had come to love and have compassion for the elderly.
On the Relation between Syntactic Divergence and Zero-Shot Performance

We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting.While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges-a gap we aim to address.As a test case, we take the transfer of Universal Dependencies (UD) parsing from English to a diverse set of languages and conduct two sets of experiments.In one, we analyze zero-shot performance based on the extent to which English source edges are preserved in translation.In another, we apply three linguistically motivated transformations to UD, creating more cross-lingually stable versions of it, and assess their zero-shot parsability.In order to compare parsing performance across different schemes, we perform extrinsic evaluation on the downstream task of cross-lingual relation extraction (RE) using a subset of a popular English RE benchmark translated to Russian and Korean. 1 In both sets of experiments, our results suggest a strong relation between cross-lingual stability and zero-shot parsing performance.
Evaluation of Improvement of Information Teaching Ability Based on Complex Online Network

The evaluation of the improvement of information teaching ability (ITA) helps teachers to proactively learn advanced information teaching techniques and to realize innovative information teaching. The relevant studies mainly focus on the theoretical level and rarely deal with the growth of teachers through learning. Therefore, this paper presents an evaluation method for ITA improvement based on complex online network. Firstly, an analysis framework was established for the activity of the ITA improvement network (ITAIN) and used to measure the ITAIN activity under the learning condition of complex online network. Following the evaluation strategy of preference ranking organization method for enrichment evaluation (PROMETHEE), the author constructed an evaluation model for the improvement of ITA and an evaluation index system (EIS) of five primary indices. The proposed model was proved effective through experiments.
The MARA corpus: Expressivity in end-to-end TTS systems using synthesised speech data

This paper introduces the MARA corpus, a large expressive Romanian speech corpus containing over 11 hours of high-quality data recorded by a professional female speaker. The data is orthographically transcribed, manually segmented at utterance level and semi-automatically aligned at phone-level. The associated text is processed by a complete linguistic feature extractor composed of: text normalisation, phonetic transcription, syllabification, lexical stress assignment, lemma extraction, part-of-speech tagging, chunking and dependency parsing.Using the MARA corpus, we evaluate the use of synthesised speech as training data in end-to-end speech synthesis systems. The synthesised data copies the original phone duration and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> patterns of the most expressive utterances from MARA. Five systems with different sets of expressive data are trained. The objective and subjective results show that the low quality of the synthesised speech data is averaged out by the synthesis network, and that no statistically significant differences are found between the systems’ expressivity and naturalness evaluations.
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Most research on novel techniques for 3D Medical Image Segmentation (MIS) is currently done using Deep Learning with GPU accelerators. The principal challenge of such technique is that a single input can easily cope computing resources, and require prohibitive amounts of time to be processed. Distribution of deep learning and scalability over computing devices is an actual need for progressing on such research field. Conventional distribution of neural networks consist in “data parallelism”, where data is scattered over resources (e.g., GPUs) to parallelize the training of the model. However, “experiment parallelism” is also an option, where different training processes (i.e., on a hyper-parameter search) are parallelized across resources. While the first option is much more common on 3D image segmentation, the second provides a pipeline design with less dependence among parallelized processes, allowing overhead reduction and more potential scalability. In this work we present a design for distributed deep learning training pipelines, focusing on multi-node and multi-GPU environments, where the two different distribution approaches are deployed and benchmarked. We take as proof of concept the 3D U-Net architecture, using the MSD Brain Tumor Segmentation dataset, a state-of-art problem in medical image segmentation with high computing and space requirements. Using the BSC MareNostrum supercomputer as benchmarking environment, we use TensorFlow and Ray as neural network training and experiment distribution platforms. We evaluate the experiment speed-up when parallelizing, showing the potential for scaling out on GPUs and nodes. Also comparing the different parallelism techniques, showing how experiment distribution leverages better such resources through scaling, e.g. by a speed-up factor from x12 to x14 using 32 GPUs. Finally, we provide the implementation of the design open to the community, and the non-trivial steps and methodology for adapting and deploying a MIS case as the here presented.
Joining forces of voice and facial biometrics: a case study in the scope of NIST SRE19

Chapter Contents: 9.1 Introduction to the NIST SRE19 challenge 9.1.1 The SRE19 CTS challenge 9.1.2 The SRE19 multimedia challenge 9.1.3 SRE19 evaluation metrics 9.2 TSP speaker verification system for the SRE19 evaluation 9.2.1 A brief review of state of the art in speaker verification 9.2.2 TSP speaker verification common pipeline for the SRE19 CTS and multimedia challenges 9.2.3 TSP speaker verification system for the SRE19 CTS challenge 9.2.4 TSP speaker verification system for the SRE19 multimedia challenge 9.2.5 Results for TSP speaker verification systems on the SRE19 CTS and multimedia challenges 9.2.6 Conclusions 9.3 TSP face recognition system for SRE19 9.3.1 Survey of face recognition systems 9.3.2 TSP face recognition system pipeline 9.3.3 Databases used in the TSP face recognition system 9.3.4 Face preprocessing 9.3.5 Embedding extractor 9.3.6 Conclusions 9.4 Audiovisual biometric system for the SRE19 multimedia challenge 9.5 Conclusions and perspectives Acknowledgments References
Question Answering System Analysis Based on Machine Learning

The question answering system is an essential task of natural language processing. In recent years, the deep neural network has made remarkable achievements in many fields. It has reached a level close to human beings in many applications, such as image classification. However, due to the particularity of the question-answering task, the question answering system close to the intelligence level is still a challenge. Firstly, this paper reviews the development of a question answering system, including the Turing Test and traditional question answering systems. Then, the advanced question answering system at present is introduced and analyzed in detail. In addition, we discussed the challenges faced by the current question answering system and puts forward some constructive solutions, including the bottleneck of statistical learning methods, data privacy, and other issues.
The Verification Method of Low Voltage Station Topology Recognition Based on DTW Algorithm

Aiming at the problems of high cost of manual investigation, low accuracy and difficult side-to-end information interaction in the verification of the relationship between distribution transformer terminal and household transformer in low-voltage substation area, a low-voltage substation area topology identification verification method based on DTW algorithm is proposed. According to the time series voltage data of smart meter and distribution transformer terminal, this paper uses DTW algorithm to calculate the similarity of time and voltage series, and checks the user by setting threshold. Then KNN algorithm is used to determine the station area, and the household transformer relationship is determined. Finally, according to the time series voltage data collected by the meter, the DTW algorithm is used to calculate the voltage sequence similarity between users, and the feeder position of users is located combined with the topology. Experiments show that the method proposed in this paper has the characteristics of high accuracy and high efficiency, and it can accurately verify the relationship between household and transformer of distribution transformer terminal equipment in low voltage substation area.
Free Modal Riesz Spaces are Archimedean: A Syntactic Proof

We prove, using syntactical proof–theoretic methods, that free modal Riesz spaces are Archimedean. Modal Riesz spaces are Riesz spaces (real vector lattices) endowed with a positive linear 1–decreasing operator, and have found application in the development of probabilistic temporal logics in the field of formal verification. All our results have been formalised using the Coq proof assistant.
Weight distributions and weight hierarchies of a family of p-ary linear codes

The weight distribution and weight hierarchy of a linear code are two important research topics in coding theory. In this paper, by choosing proper defining sets from inhomogeneous quadratic functions over $${\mathbb {F}}_{q}^{2}$$ , we construct a family of three-weight p-ary linear codes and determine their weight distributions and weight hierarchies. Most of the codes can be used in secret sharing schemes.
Verifiable Homomorphic Encrypted Computations for Cloud Computing

Cloud computing is becoming an essential part of computing, especially for enterprises.As the need for cloud computing increases, the need for cloud data privacy, confidentially, and integrity are also becoming essential.Among potential solutions, homomorphic encryption can provide the needed privacy and confidentiality.Unlike traditional cryptosystem, homomorphic encryption allows computation delegation to the cloud provider while the data is in its encrypted form.Unfortunately, the solution is still lacking in data integrity.While on the cloud, there is a possibility that valid homomorphically encrypted data beings swapped with other valid homomorphically encrypted data.This paper proposes a verification scheme based on the modular residue to validate homomorphic encryption computation over integer finite field to be used in cloud computing so that data confidentiality, privacy, and data integrity can be enforced during an outsourced computation.The performance of the proposed scheme varied based on the underlying cryptosystems used.However, based on the tested cryptosystems, the scheme has 1.5% storage overhead and a computational overhead that can be configured to work below 1%.Such overhead is an acceptable trade-off for verifying cloud computation which is highly needed in cloud computing.
Usability Evaluation of the Restaurant Finder Application Using Inspection and Inquiry Methods

Restaurant Finder application is a mobile application that helps users to find restaurants according to their requirements. One of the most well-known and widely used restaurant finder apps is Zomato. Zomato ranks first in the food and delivery category websites from around the world. But, the number of users of a product or application certainly does not guarantee the satisfaction of the user experience of apps. A system that is poorly designed will make it difficult for users, which results in the system being rarely or misused so that the costs to the organization become high and dangerous to the reputation of the company. The purpose of this study was to evaluate the usability of the restaurant finder application using inspection and inquiry methods. Inspection method using the Enhanced Cognitive Walkthrough method, and for inquiry methods using the User Experience Questionnaire method and the System Usability Scale. The inspection method results identified 7 serious problems. Recommendations for improvement based on inspection methods are focused on login tasks, reviews, and food promo information. The inquiry methods results identified 5 aspects that needed improvement. The five aspects consist of novelty, stimulation, learnability, consistency, complexity.