Reviewerly/exHarmony huggingface dataset:
Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation

Most recent semisupervised video object segmentation (VOS) methods rely on fine-tuning deep convolutional neural networks online using the given mask of the first frame or predicted masks of subsequent frames. However, the online fine-tuning process is usually time-consuming, limiting the practical use of such methods. We propose a directional deep embedding and appearance learning (DDEAL) method, which is free of the online fine-tuning process, for fast VOS. First, a global directional matching module (GDMM), which can be efficiently implemented by parallel convolutional operations, is proposed to learn a semantic pixel-wise embedding as an internal guidance. Second, an effective directional appearance model-based statistics is proposed to represent the target and background on a spherical embedding space for VOS. Equipped with the GDMM and the directional appearance model learning module, DDEAL learns static cues from the labeled first frame and dynamically updates cues of the subsequent frames for object segmentation. Our method exhibits the state-of-the-art VOS performance without using online fine-tuning. Specifically, it achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ J}}$ </tex-math></inline-formula> & <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ F}}$ </tex-math></inline-formula> mean score of 74.8% on DAVIS 2017 data set and an overall score <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ G}}$ </tex-math></inline-formula> of 71.3% on the large-scale YouTube-VOS data set, while retaining a speed of 25 fps with a single NVIDIA TITAN Xp GPU. Furthermore, our faster version runs 31 fps with only a little accuracy loss.
A Hybrid Approach to Formal Verification of Higher-Order Masked Arithmetic Programs

Side-channel attacks, which are capable of breaking secrecy via side-channel information, pose a growing threat to the implementation of cryptographic algorithms. Masking is an effective countermeasure against side-channel attacks by removing the statistical dependence between secrecy and power consumption via randomization. However, designing efficient and effective masked implementations turns out to be an error-prone task. Current techniques for verifying whether masked programs are secure are limited in their applicability and accuracy, especially when they are applied. To bridge this gap, in this article, we first propose a sound type system, equipped with an efficient type inference algorithm, for verifying masked arithmetic programs against higher-order attacks. We then give novel model-counting-based and pattern-matching-based methods that are able to precisely determine whether the potential leaky observable sets detected by the type system are genuine or simply spurious. We evaluate our approach on various implementations of arithmetic cryptographic programs. The experiments confirm that our approach outperforms the state-of-the-art baselines in terms of applicability, accuracy, and efficiency.
Predicting mental health of prisoners by artificial neural network

Introduction: Maintaining and improving prisoners' health and their rehabilitation can be effective steps towards eliminating health inequalities and approaching the UN's Sustainable Development Goals.Accordingly, identifying protective factors and health barriers of this vulnerable group and changing the prison into an environment that can deliver health interventions tailored to the needs of inmates can provide the basis for attaining justice in health.Purpose: This study builds on an artificial neural network model to determine the effect of demographic, psychological, criminological, and physical activity factors on prisoners' general health.Methods: The study detected the patterns between variables using a neural network with nine inputs and one output.To determine the neural network with the minimum sum of squared errors, we evaluated the performance of all neural networks using varying algorithms and numbers of neurons in the hidden layer.For this purpose, the analysis of the data of 149 prisoners aged between 16 and 61 years was performed using SPSS-22 software.Results: The optimal neural network model was useful in predicting prisoners' general health.In this model, the variables of occupation, life expectancy, age, and hope of acceptance were identified as the first most significant factors with 19.25, 17.45, 15.98, and 15.16 percentages, respectively, whereas the cause of incarceration, education level, type of drug misuse, and physical activity were the second most important factors with 8.82, 8.38, 7.91, and 7.04 percentages, respectively.Conclusion: Experiencing psychosocial pressures related to incarceration is more severe for the marginalized and disadvantaged individuals, persons in very young or old age ranges, and those with no history of incarceration, which can increase the likelihood of impaired health for these inmates.Consideration of the prisoners' needs in proportion to their characteristics and provision of emotional and spiritual support of the inmates, especially in the early stages of incarceration, can help shape an effective adjustment process and select appropriate and efficient strategies.
Deceptive Deletions for Protecting Withdrawn Posts on Social Media Platforms

Over-sharing poorly-worded thoughts and personal information is prevalent on online social platforms.In many of these cases, users regret posting such content.To retrospectively rectify these errors in users' sharing decisions, most platforms offer (deletion) mechanisms to withdraw the content, and social media users often utilize them.Ironically and perhaps unfortunately, these deletions make users more susceptible to privacy violations by malicious actors who specifically hunt post deletions at large scale.The reason for such hunting is simple: deleting a post acts as a powerful signal that the post might be damaging to its owner.Today, multiple archival services are already scanning social media for these deleted posts.Moreover, as we demonstrate in this work, powerful machine learning models can detect damaging deletions at scale.Towards restraining such a global adversary against users' right to be forgotten, we introduce Deceptive Deletion, a decoy mechanism that minimizes the adversarial advantage.Our mechanism injects decoy deletions, hence creating a two-player minmax game between an adversary that seeks to classify damaging content among the deleted posts and a challenger that employs decoy deletions to masquerade real damaging deletions.We formalize the Deceptive Game between the two players, determine conditions under which either the adversary or the challenger provably wins the game, and discuss the scenarios in-between these two extremes.We apply the Deceptive Deletion mechanism to a real-world task on Twitter: hiding damaging tweet deletions.We show that a powerful global adversary can be beaten by a powerful challenger, raising the bar significantly and giving a glimmer of hope in the ability to be really forgotten on social platforms.
Is it enough to optimize CNN architectures on ImageNet?

Classification performance based on ImageNet is the de-facto standard metric for CNN development. In this work we challenge the notion that CNN architecture design solely based on ImageNet leads to generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains. To this end, we investigate and ultimately improve ImageNet as a basis for deriving such architectures. We conduct an extensive empirical study for which we train 500 CNN architectures, sampled from the broad AnyNetX design space, on ImageNet as well as 8 additional well-known image classification benchmark datasets from a diverse array of application domains. We observe that the performances of the architectures are highly dataset dependent . Some datasets even exhibit a negative error correlation with ImageNet across all architectures. We show how to significantly increase these correlations by utilizing ImageNet subsets restricted to fewer classes . These contributions can have a profound impact on the way we design future CNN architectures and help alleviate the tilt we see currently in our community with respect to over-reliance on one dataset.
Scheduling and Path-Planning for Operator Oversight of Multiple Robots

There is a need for semi-autonomous systems capable of performing both automated tasks and supervised maneuvers. When dealing with multiple robots or robots with high complexity (such as humanoids), we face the issue of effectively coordinating operators across robots. We build on our previous work to present a methodology for designing trajectories and policies for robots such that a few operators can supervise multiple robots. Specifically, we: (1) Analyze the complexity of the problem, (2) Design a procedure for generating policies allowing operators to oversee many robots, (3) Present a method for designing policies and robot trajectories to allow operators to oversee multiple robots, and (4) Include both simulation and hardware experiments demonstrating our methodologies.
Multi-Label Learning Based on Transfer Learning and Label Correlation

In recent years, multi-label learning has received a lot of attention. However, most of the existing methods only consider global label correlation or local label correlation. In fact, on the one hand, both global and local l... | Find, read and cite all the research you need on Tech Science Press
Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm

With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given.
TEACHING WRITING THROUGH CANVA APPLICATION

This study aims to investigate the effectiveness of using Canva application media to enhance students’ writing skills. This research was conducted in July-August at Mts Al-Islamiyah, Ciledug. Researchers used a quantitative method which is pre-experimental research design by applying pre-test and post-test. Writing tests were employed in collecting students’ achievement as instruments of this study. Researchers used one class in class IX-1 MTs Al-Islamiyah Ciledug as research samples. Determining samples of this study used the random sampling technique. To analyze the research data, researcher applied t-test. Based on the research analysis, the mean score of students’ writing test on the post-test was 70.83, and the pre-test was 64.73. The results also showed that the T-cal value (9,585) was higher than the Ttable (2.021), at the 5% significance level. If the result t-test was higher than t-table. It indicated that the alternative hypothesis (Ha) was accepted, and (H0) was rejected. Based on the results of the t-test calculation, this current study can be concluded that the Canva Application is an effective media to facilitate students to improve their writing performance. For further studies, English teachers can imitate the basic theory of this study as consideration to teach writing skills.
The Effect of Organizational Culture on Knowledge Management and Managerial Performance of Government Department in Dubai

This study aim to emphasize the importance of knowledge management and the factors that influence the knowledge management within Dubai public sector organization. The study is to highlight the effect of organizational culture on the relationship between knowledge management and managerial performance of government departments in Dubai. Development of organizational culture is pegged on managerial performance. The role of the manager in any department is to evaluate the system and eliminate the impediments to success.As long as the mindset is built on competitive spirit, employees will be nurtured to appreciate superior performance amid their challenges. The manager should also act as a problem solver by understanding the existing challenges and using the available knowledge to create feasible solutions Data were collected by distributing questionnaires to 215 systematic randomly sampled respondents from seven public departments. Data collected were analyzed by using descriptive and inferential statistics. The research showed that the effect of organizational culture on knowledge management and managerial performance of government department in Dubai.
Multilingual Entity and Relation Extraction Dataset and Model

We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction.The SMi-LER dataset consists of 1.1 M annotated sentences, representing 36 relations, and 14 languages.To the best of our knowledge, this is currently both the largest and the most comprehensive dataset of this type.We introduce HERBERTa, a pipeline that combines two independent BERT models: one for sequence classification, and the other for entity tagging.The model achieves micro F 1 81.49for English on this dataset, which is close to the current SOTA on CoNLL, SpERT.
Solar PV Tree: Shade-Free Design and Cost Analysis Considering Indian Scenario

In this paper, the performance and the cost-effectiveness of a solar PV tree for supplying the energy demand of a flood lighting system at a basketball court in the School of Engineering and Technology, Christ (Deemed to be University) at Bangalore, India, are analyzed. Also, the energy demand of a flood lighting system for year 2017 is estimated (16 kWh/day), and the design of 4 individual trees of 1 kWp each is proposed, which saves around 40 sq.m area of land near to the basketball court. The experimental data was collected from June 1st, 2018 to May 31st, 2019, using a data acquisition system and processed to calculate the monthly cost of energy produced by each tree. In order to reduce the complexity in design and allow it to be shade-free, all the panels of a tree were oriented at the same azimuth angle. Based on technical and economical assessments with respect to rooftop systems, the solar PV tree presented reasonable results and could be a future adoptable technology for high population density areas, as well as for remote applications. Later, the adoptability of the proposed solar PV tree was simulated for 2 kWp, considering the climatic conditions of 2020, for different rural and urban locations of India. From the techno-economic-environmental analysis, it is highlighted that the annual energy yield is more with the solar PV tree model than with a land-mounted SPV system. The cost savings and greenhouse gas (GHG) reduction are also higher with the proposed oak tree-based solar PV tree in urban areas than in rural areas recommending it for practical applications.
POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users’ deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users’ geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.
Web Application Penetration Testing

This paper describes the in-depth technical approach to perform manual penetration test in web applications for testing the integrity and security of the application and also serves as a guide to test OWASP top 10 security vulnerabilities. The paper is more focused on providing detailed knowledge about manual web application penetration testing methodologies in order to secure them from malicious black hat hackers.
An Approach for Automatic Ontology Enrichment from Texts

The automatic ontology enrichment consists of automatic knowledge extraction from texts related to a domain of discourse in the aim to enrich automatically an initial ontology of the same domain. However, the passage, from a plain text to an enriched ontology requires a number of steps. In this paper, we present a three steps ontology enrichment approach. In the first step, we apply natural language processing techniques to obtain tagged sentences. The second step allows us to reduce each extracted sentence to an SVO (Subject, Verb, and Object) sentence, supposed to preserve main information carried by the original sentence(s) from which it is extracted. Finally, in the third step, we proceed to enrich an initial ontology built manually by adding extracted terms in the generated SVO as new concepts or instances of concepts and new relations. To validate our approach, we have used “Phytotherapy" domain because of the availability of related texts on the WWW and also because its usefulness for pharmaceutical industry. The first results obtained, after experiments on a set of different texts, testify the performance of the proposed approach.
Ensuring Text and Data Mining: Remaining Issues With the EU Copyright Exceptions and Possible Ways Out

Text and Data Mining (TDM) is a vital tool in the Big Data economy. TDM uses techniques from natural language processing, machine learning, information retrieval, and knowledge management for the automated analysis of digital content (structured and unstructured data), in order to extract information, identify patterns, discover new trends, insights or correlations.The importance of TDM has been understood by the European legislator, which has introduced two specifically tailored exceptions in the Copyright in the Digital Single Market Directive. After a critical analysis of the new provisions, the paper argues that they still present several flaws that risk to stifle AI developments in Europe. Thus, the contribution outlines an interpretative framework, based on the analysis of the infringement test, to rethink the rights of reproduction and extraction in line with the economic rationale of copyright and the database right. Furthermore, the paper makes suggestions to improve the TDM exceptions at national level. In conclusion, it points out the remaining challenges of private ordering and trade secrets for research and AI innovation.
Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

The flexibility provided by the cloud service provider at reduced cost popularized the cloud tremendously. The cloud service provider must schedule the incoming requests dynamically. In a cloud environment tasks must be scheduled such that proper resource utilization is achieved. Hence task scheduling plays a significant role in the functionality and performance of cloud computing systems. While there exist many approaches for boosting the task scheduling in the cloud, it is still an unresolved issue. In this proposed framework we attempt to optimize the usage of cloud computing resources by applying machine learning techniques. The new proposed framework dynamically selects the scheduling algorithm for the incoming request rather than arbitrary assigning a task to the scheduling algorithm. The scheduling algorithm is predicted dynamically using a neural network which is the best for the incoming request. The proposed framework considers scheduling parameters namely cost, throughput, makespan and degree of imbalance. The algorithms chosen for scheduling are 1) MET 2) MCT 3) Sufferage 4)Min-min 5) Min-mean 6) Min-var. The framework includes 4 neural networks to predict the best algorithm for each scheduling parameters considered for optimization. PCA algorithm is used for extracting relevant features from the input data set. The proposed framework shows the scope for the overall system performance by dynamically selecting precise scheduling algorithms for each incoming request from the user.
Informative Multimodal Unsupervised Image-to-Image Translation

We propose a new method of multimodal image translation, called InfoMUNIT, which is an extension of the state-of-the-art method MUNIT. Our method allows controlling the style of the generated images and improves their quality and diversity. It learns to maximize the mutual information between a subset of style code and the distribution of the output images. Experiments show that our model cannot only translate one image from the source domain to multiple images in the target domain but also explore and manipulate features of the outputs without annotation. Furthermore, it achieves a superior diversity and a competitive image quality to state-of-the-art methods in multiple image translation tasks.
Fast Fallback Watermark Detection Using Perceptual Hashes

Forensic watermarking is often used to enable the tracing of digital pirates that leak copyright-protected videos. However, existing watermarking methods have a limited robustness and may be vulnerable to targeted attacks. Our previous work proposed a fallback detection method that uses secondary watermarks rather than the primary watermarks embedded by existing methods. However, the previously proposed fallback method is slow and requires access to all watermarked videos. This paper proposes to make the fallback watermark detection method faster using perceptual hashes instead of uncompressed secondary watermark signals. These perceptual hashes can be calculated prior to detection, such that the actual detection process is sped up with a factor of approximately 26,000 to 92,000. In this way, the proposed method tackles the main criticism about practical usability of the slow fallback method. The fast detection comes at the cost of a modest decrease in robustness, although the fast fallback detection method can still outperform the existing primary watermark method. In conclusion, the proposed method enables fast and more robust detection of watermarks that were embedded by existing watermarking methods.