Reviewerly/exHarmony huggingface dataset:
The Role of Electronic Management in Reducing Organizational Work Stress: A Field Research at AlMaarif University College

This research aims to identify correlation and influence between electronic management and organizational work stress in Al-Maarif University College, the importance of the research emerged from the importance of the applied aspect of the research, as it was applied in the field of higher education and Al-Maarif University College, where higher education is considered one of the vital and important sectors in society. The research sample consisted of (39) members of the college’s staff and administrative staff, The questionnaire was used as a main tool for collecting information and data, and it was entered, sorted and analyzed statistically by the (SPSS V.20) program, And the results are proven a high level of presence and practices of electronic management dimensions the results also showed a low level of organizational work stress in the work environment, The results also showed the existence of correlation and effect relationships between the research variables, As for the most important recommendations reached by the need to enhance the use of management information systems and databases in the college, In addition to specialized programs that contribute to alleviating the burden and reducing work stress on scientific departments, administrative units and their employees.
A novel knowledge extraction method based on deep learning in fruit domain

Knowledge extraction aims to identify entities and extract relations between them from unstructured text, which are in the form of triplets. Analysis of the fruit nutrition domain corpus revealed many overlapping triplets, that is, multiple correspondences between a subject and multiple objects or the same subject and object. The current relevant methods mainly target the extraction of ordinary triplets, which cannot accurately identify overlapping triplets. To solve this problem, a deep learning based model for overlapping triplet extraction is proposed in this study. The relation is modeled as a function that maps a subject to an object. The hybrid information of the subject is entered into the relation-object extraction model to detect the object and relation. The experimental results show this model outperforms existing extraction models and achieves state-of-the-art performance on the manually labeled fruit nutrition domain dataset. In terms of application value, the proposed work can obtain a high-quality and structured fruit nutrition knowledge base, which provides application fundamentals for downstream applications of nutrition matching.
Development of Mobile Application that Detects Phishing Messages to Decrease the Percentage of Data Theft during the COVID-19 Pandemic

The COVID-19 pandemic has established not only a health emergency, but has generated an emergency in the control of personal data of all those people who make use of technological means, which increased the activity of phishing which consists of the theft of personal data through the circulation of false information through the different social networks, in addition, the circulation with messages related to the cure of this disease only for the theft of data. This research develops a mobile application that detects malicious URLs found within the content of textmessages. The developed application performs an analysis of the URLs according to the database that is updated with each attack detected, performing a blocking of the content and notifies the user of the actions that canbetaken, with this the theft of the personal data of the users is avoided. This application is very useful for all those people who use mobile equipment (mobile) and have no knowledge of these types of attacks, since they are likely to perform the actions that the perpetrators foresee for the obtaining of their personal data, so this application provides a means of security against these types of phishing attacks.
Joint target tracking and identification in complex traffic scene

Abstract In practical road traffic scene, targets usually face high ground clutter, high and variable motion, high nonlinearity, which lead to targets tracking or identification challenging. What’s more, tracking and identification are usually interdependent in reality, and thus it is promising to solve them jointly. In this paper, we propose a novel joint tracking and identification (JTI) scheme to handle such problems involving coupled tracking and identification, i.e., JTI problems. Specifically, we formulate the JTI problem in complex traffic scene using a hybrid system. Then, by exploiting the generalized Bayes risk for JTI, we derive analytical estimator and decider for the coupling of tracking and identification in complex road targets motion. Furthermore, an unscented Kalman filter-expected mode augmentation-based estimation strategy is creatively developed to improve both estimation and decision performance. In additions, a joint performance evaluation metric is presented to assess the performance the joint of the proposed JTI scheme. Finally, two simulation examples under different traffic scenarios demonstrate that the proposed JTI approach outperforms the traditional tracking-then-identification and identification-then-tracking methods in joint performance.
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access

To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance. In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-end response generation model. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history. Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.
MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles

Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations. Scholarly information extraction has various applications in exploration, archival, and curation services for digital libraries and knowledge management systems. We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles. Our approach condenses the article's full-text to property-value pairs as a segmented text snippet called structured summary. We also present a sizable scholarly dataset combining structured summaries retrieved from a scholarly knowledge graph and corresponding publicly available scientific articles, which we openly publish as a resource for the research community. Our results show that structured summarization is a suitable approach for targeted information extraction that complements other commonly used methods such as question answering and named entity recognition.
On the distribution alignment of propagation in graph neural networks

Graph neural networks (GNNs) have been widely adopted for modeling graph-structure data. Most existing GNN studies have focused on designing different strategies to propagate information over the graph structures. After systematic investigations, we observe that the propagation step in GNNs matters, but its resultant performance improvement is insensitive to the location where we apply it. Our empirical examination further shows that the performance improvement brought by propagation mostly comes from a phenomenon of distribution alignment, i.e., propagation over graphs actually results in the alignment of the underlying distributions between the training and test sets. The findings are instrumental to understand GNNs, e.g., why decoupled GNNs can work as good as standard GNNs.1
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning

Large-scale training have propelled significant progress in various sub-fields of AI such as computer vision and natural language processing. However, building robot learning systems at a comparable scale remains challenging. To develop robots that can perform a wide range of skills and adapt to new scenarios, efficient methods for collecting vast and diverse amounts of data on physical robot systems are required, as well as the capability to train high-capacity policies using such datasets. In this work, we propose a framework for scaling robot learning, with specific focus on multi-task and multi-scene manipulation in kitchen environments, both in simulation and in the real world. Our proposed framework, CACTI, comprises four stages that separately handle: data collection, data augmentation, visual representation learning, and imitation policy training, to enable scalability in robot learning . We make use of state-of-the-art generative models as part of the data augmentation stage, and use pre-trained out-of-domain visual representations to improve training efficiency. Experimental results demonstrate the effectiveness of our approach. On a real robot setup, CACTI enables efficient training of a single policy that can perform 10 manipulation tasks involving kitchen objects, and is robust to varying layouts of distractors. In a simulated kitchen environment, CACTI trains a single policy to perform 18 semantic tasks across 100 layout variations for each individual task. We will release the simulation task benchmark and augmented datasets in both real and simulated environments to facilitate future research.
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-motor control. Model-based RL has the potential to be highly sample efficient by concurrently learning a world model and using synthetic rollouts for planning and policy improvement. However, in practice, sample-efficient learning with model-based RL is bottlenecked by the exploration challenge. In this work, we find that leveraging just a handful of demonstrations can dramatically improve the sample-efficiency of model-based RL. Simply appending demonstrations to the interaction dataset, however, does not suffice. We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework. We empirically study three complex visuo-motor control domains and find that our method is 150%-250% more successful in completing sparse reward tasks compared to prior approaches in the low data regime (100K interaction steps, 5 demonstrations). Code and videos are available at: https://nicklashansen.github.io/modemrl
A Survey and Comparison of Industrial and Academic Research on the Evolution of Software Product Lines

Past research on software product lines has focused on the initial development of reusable assets and related challenges, such as cost estimation and implementation issues. Naturally, as software product lines are increasingly adopted throughout industry, their ongoing maintenance and evolution are getting more attention as well. However, it is not clear to what degree research is following this trend, and where the interests and demands of the industry lie. In this technical report, we provide a survey and comparison of selected publications on software product line maintenance and evolution at SPLC. In particular, we analyze and discuss similarities and differences of these papers with regard to their affiliation with industry and academia. From this, we infer directions for future research that pave the way for systematic and organized evolution of software product lines, from which industry may benefit as well.
LR B-Splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids

Abstract Performing surface approximation of geospatial point clouds with locally refined (LR) B-splines comes with several challenges: (i) Point clouds have varying data density, (ii) outliers should be eliminated without deleting features, (iii) voids, also called holes, or data gaps should be treated specifically to avoid the drop of the approximated surface in domains without points. These factors tend to be even more challenging when point clouds acquired from different sensors having different noise characteristics are fused together. The data set becomes non-uniform and the fusing process itself involves a risk of an increased noise level. In this chapter, we provide some tools to answer those specific challenges. We will use terrain and seabed data and show didactically how to perform adaptive surface approximation with local refinement and to select customized parameters. We will further address the problem of choosing an appropriate tolerance for performing an adaptive fitting, and discuss the refinement strategies within the context of LR B-splines. The latter is shown to provide a promising framework for surface fitting of heterogeneous point clouds from various sources.
Adaptive aggregation and dynamic self-learning for multi-view stereo network

For the problem of poor accuracy and integrity of multi-view stereo (MVS) reconstruction, we propose an efficient multi-view stereo network, which mainly studies feature extraction module and depth map optimization module. At first, we introduce adaptive aggregation module to use context-aware convolution and multi-scale aggregation to adaptively extract image features, which effectively improves the extraction accuracy of weak texture surface. Then, we introduce dynamic self-learning optimization module (DSL) to solve the problem of depth map over-smoothing. Experiments show that, compared with MVSNet, our network greatly improves the integrity of reconstruction, and does not increase memory and time overhead.
Innovative design for electrical outlets in ship's living quarters

To solve the problem of ship living area emergency cases, the ship and offshore illumination conditions, unable to timely find the socket pin and insert for power supply of equipment, application of TIRZ design theory, triangle TIRZ analysis model is set up, the innovation design a wall-mounted recessed guided insertion neat wiring strip. The product sample processing and customer experience using experiments were carried out. The experimental results show that the plug can quickly guide the operator to insert the circular plug into the target jack, which avoids electric shock, reduces anxious groping in the dark, and greatly improves the life efficiency and convenience of the crew.
Summing $$\mu (n)$$: a faster elementary algorithm

We present a new elementary algorithm that takes timeOϵx35(logx)85+ϵandspaceOx310(logx)1310 (measured bitwise) for computing M(x)=∑n≤xμ(n), where μ(n) is the Möbius function. This is the first improvement in the exponent of x for an elementary algorithm since 1985. We also show that it is possible to reduce space consumption to O(x1/5(logx)5/3) by the use of (Helfgott in: Math Comput 89:333-350, 2020), at the cost of letting time rise to the order of x3/5(logx)2loglogx .
Research on monitoring technology of mud-water shield crossing the Weihe River based on semi-flexible inclined settlement benchmarks

With the development of China's transportation rail network, there are more and more cross-river tunnels. To solve the monitoring problem that the settlement benchmarks used for river bed deformation are prone to incline and result in large measurement errors during the construction of the shield tunnel under the river, this paper takes Xi'an Metro Line 1 under the double shield tunnel in the Weihe river engineering as an example and introduces the total station combined with semi-flexible inclined settlement benchmarks static observation methods in the application of the riverbed deformation monitoring for a long time. The observation results show that during the crossing of the leading line, the riverbed settlement above the left and right lines was always controlled within a reasonable range (within 6 mm), and the total settlement of the measuring points on the left line was slightly larger than that on the right line.
Performance Evaluation of Local Detectors in the Presence of Noise for Multi-Sensor Remote Sensing Image Matching

Performance Evaluation of Local Detectors in the Presence of Noise for Multi-Sensor Remote Sensing Image Matching
Rapid statistical learning of object part co-occurrence in humans and monkeys

Some objects frequently co-occur in our environment, such as a mortar and pestle. While we quickly learn such statistical regularities, whether they confer any task advantage remains unclear. Here, we investigated this issue using parallel experiments in humans and monkeys. We created two sets of objects with spatially separated parts at three possible locations A/B/C. In Set 1, we selected 24 specific shape pairs at locations A-B to combine with 24 shapes at location C, to create 576 trigram objects. Likewise in Set 2, 24 shapes at location A were combined in all possible ways with 24 specific shape pairs at locations B-C to create 576 trigram objects. We therefore reasoned that repeated viewing of Set 1 or Set 2 should result in learning of specific part combinations (AB for Set 1, BC for Set 2, as they repeat 24 times more) over others. In Experiment 1, 16 human participants passively viewed all trigram objects from either Set 1 or Set 2 only once, and then were asked to choose the shape pair (AB vs BC) that was more familiar. We found that in both groups, participants identified the familiar pair with higher accuracy (average accuracy: 61.45%, p < 0.01, sign-rank test across 16 participants). In Experiment 2, two monkeys were familiarized during passive viewing to Set 1 or Set 2, and then tested on a same-different task with trigram objects in which the AB (e.g., A1B1C1 to A2B2C1) or BC (e.g., A1B1C1 to A1B2C2) locations changed. We found that each monkey detected a familiar pair breaking more accurately (average accuracy: 90%) than unfamiliar pair breaking (average accuracy: 73%, p < 0.05, unpaired t-test across pairs). Taken together, our results show that both humans and monkeys show rapid learning of statistical regularities, and that this results in faster object discrimination.
Optimizers in Deep Learning: A Comparative Study and Analysis

Abstract: Machine learning has enormously contributed towards optimization techniques with new ways for optimization algorithms. These approaches in deep learning have wide applications with resurgence of novelty starting from Stochastic Gradient Descent to convex and non-convex ones. Selecting an optimizer is a vital choice in deep learning as it determines the training speed and final performance predicted by the DL model. The complexity further increases with growing deeper due to hyper-parameter tuning and as the data sets become larger. In this work, we analyze most popular and widely optimizers algorithms empirically. The augmenting behaviors of these are tested on MNIST, Auto Encoder data sets. We compare them pointing out their similarities, differences and likelihood of their suitability for a given applications. Recent variants of optimizers are highlighted. The article focuses on their critical role and pinpoints which one would be a better option while making a trade-off.
Deep Image Style Transfer from Freeform Text

This paper creates a novel method of deep neural style transfer by generating style images from freeform user text input. The language model and style transfer model form a seamless pipeline that can create output images with similar losses and improved quality when compared to baseline style transfer methods. The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output. A proof-of-concept tool is also developed to integrate the models and demonstrate the effectiveness of deep image style transfer from freeform text.
Wearable Technology Application for Banjar Language Translator to Support Ease of Communication: Prototyping and Sustainability Opportunities

Language differences are one of the obstacles to communication. Especially if you use certain regional languages such as Banjar Language. Banjar Language is the mother tongue used by the Banjar tribe in South Kalimantan Province, Indonesia. South Kalimantan is known as an area that has promising tourism potential and is the main gateway of the new nation's capital city. This causes many tourists to visit (overtourism). Tourists will face obstacles when communicating with local residents of Banjar. The purpose of this research is to produce a prototype of a tool in the form of a wearable technology-based translator application, using speech recognition with Python programming, then investigate the usability and sustainability opportunities of the prototype that has been made. A total of 200 respondents have been involved consisting of domestic tourists and local residents of Banjar, these respondents were involved in exploring needs and when evaluating usability. The results show that the prototype that has been made provides usability and is feasible to proceed to the implementation stage, as a means to help tourists from the archipelago and local residents of Banjar to communicate and also transact.
Posterior-neighborhood-regularized Latent Feature Learning

Quality-of-Service (QoS) is to measure the nonfunctional characteristics of web services [1–9]. If the QoS data of candidate web services is available, you can select and recommend Web services that meet the quality of service requirements of potential users. Warm-up tests that directly invoke Web services are often performed to retrieve QoS data [10, 11]. However, in real applications, the number of candidate services is usually large. Therefore, checking all candidate Web services is expensive, time-consuming, and therefore impractical [6, 12, 13].
Search and learning for unsupervised text generation

Abstract With the advances of deep learning techniques, text generation is attracting increasing interest in the artificial intelligence (AI) community, because of its wide applications and because it is an essential component of AI. Traditional text generation systems are trained in a supervised way, requiring massive labeled parallel corpora. In this paper, I will introduce our recent work on search and learning approaches to unsupervised text generation, where a heuristic objective function estimates the quality of a candidate sentence, and discrete search algorithms generate a sentence by maximizing the search objective. A machine learning model further learns from the search results to smooth out noise and improve efficiency. Our approach is important to the industry for building minimal viable products for a new task; it also has high social impacts for saving human annotation labor and for processing low‐resource languages. Link to video abstract: https://youtu.be/Xir1e9g6oIc