Reviewerly/exHarmony huggingface dataset:
SAGMA: Secure Aggregation Grouped by Multiple Attributes

Encryption can protect data in outsourced databases -- in the cloud -- while still enabling query processing over the encrypted data. However, the processing leaks information about the data specific to the type of query, e.g., aggregation queries. Aggregation over user-defined groups using SQL's GROUP BY clause is extensively used in data analytics, e.g., to calculate the total number of visitors each month or the average salary in each department. The information leaked, e.g., the access pattern to a group, may reveal the group's frequency enabling simple, yet detrimental leakage-abuse attacks. In this work we present SAGMA -- an encryption scheme for performing secure aggregation grouped by multiple attributes. The querier can choose any combination of one or multiple attributes in the GROUP BY clause among the set of all grouping attributes. The encryption scheme only stores semantically secure ciphertexts at the cloud and query processing hides the access pattern, i.e., the frequency of each group. We implemented our scheme and our evaluation results underpin its practical feasibility.
An Approach for Unattended Object Detection through Contour Formation using Background Subtraction

Any unattended object in public places is generally considered as a suspicious object. Identification of such objects in public places is one of challenging tasks in computer vision. Robust object detection system with prompt and accurate identification of safety breaches augments security measures at public places such as shopping malls, airports, railway stations, etc. This paper proposes a new and efficient approach for the detection of unattended objects in a well illuminated environment from video captured through a single camera. The proposed approach extracts foreground objects using background subtraction method. These foreground objects are further categorized into static and moving objects by comparing their respective coordinates of centroid in consecutive frames. The foreground object is classified as an unattended object if that object is static over a predefined period of time and its size lies in the predefined range. The performance of the proposed approach is evaluated by conducting the experiments on ABandoned Objects DAtaset (ABODA) dataset and also on few real time videos recorded by the authors. The results are derived for evaluating the experiments using Correct Object Detection Rate (CODR), Object Success Rate (OSR), and False Alarm Rate (FAR) metrics. The CODR & OSR rates so attained for the proposed approach with static background are 100% each while FAR is 0%. Average CODR, average OSR, and average FAR of static and dynamic background are found to be 70.83%, 67.25%, and 35.41% respectively for the proposed approach. The proposed approach updates/replaces the original background frame to minimize the scope of false alarm rate in case of dynamic background. It is generic and can be efficiently used for object detection in various public scenarios.
Should We Consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems?

Intelligent Tutoring Systems (ITSs) usually make adaptation decisions based on user models that rely on students’ knowledge. However, there are other interesting indicators, which could be used for adaptation that need further exploration. Students’ efficiency (defined as whether they require a lot of time to achieve correctness in their exercises) and constancy (defined as whether they spend a similar time each day they take exercises in the ITS) are two of these indicators. This work aims to analyze 1) how these variables are distributed among students, 2) their evolution over time, and 3) how they are related to other outcomes. Results show that there are different profiles based on the efficiency; e.g., students with low efficiency that need a lot of time to solve exercises correctly, and low reflective students, among others. Furthermore, efficiency and constancy do not vary on average throughout the course. In addition, students are less constant in their daily time spent when their total time spent and average time per exercise is higher, and more efficient students tend to be more constant. Finally, it was found that neither efficiency nor constancy correlate with better grades. The existence of different profiles based on these variables and that they add a different dimension from student knowledge based on answer on exercises suggest that ITSs can make adaptation based on efficiency and constancy.
Real-Time Tracking of Vehicles with Siamese Network and Backward Prediction

Tracking of vehicles is a key technique for Intelligent transportation system, which commonly follows tracking-by-detection strategy. Due to high appearance similarity among vehicles and heavy occlusion caused by busy traffic flow, a major challenge in such a tracking system is the limited performance of the underlying detector which may produce noisy detections. Consequently, Siamese network and backward prediction-based vehicle tracking approach is proposed. Siamese network based forward position prediction is designed to alleviate the interference of noisy detections, while backward prediction verification is performed to reduce the false positives arising with forward prediction. The final tracklets are obtained through weighted merging based on the detection confidence and forward prediction confidence. The experiment results demonstrate that the proposed method outperforms the state-of-the-art on the UA-DETRAC vehicle tracking dataset, as well as maintains real-time processing at an average tracking speed of 20.1fps, which can be used for real-time applications.
Learning From Multi-Dimensional Partial Labels

Multi-dimensional classification has attracted huge attention from the community. Though most studies consider fully annotated data, in real practice obtaining fully labeled data in MDC tasks is usually intractable. In this paper, we propose a novel learning paradigm: MultiDimensional Partial Label Learning (MDPL) where the ground-truth labels of each instance are concealed in multiple candidate label sets. We first introduce the partial hamming loss for MDPL that incurs a large loss if the predicted labels are not in candidate label sets, and provide an empirical risk minimization (ERM) framework. Theoretically, we rigorously prove the conditions for ERM learnability of MDPL in both independent and dependent cases. Furthermore, we present two MDPL algorithms under our proposed ERM framework. Comprehensive experiments on both synthetic and real-world datasets validate the effectiveness of our proposals.
Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos

Scene perceiving and understanding tasks including depth estimation, visual odometry (VO) and camera relocalization are fundamental for applications such as autonomous driving, robots and drones. Driven by the power of deep learning, significant progress has been achieved on individual tasks but the rich correlations among the three tasks are largely neglected. In previous studies, VO is generally accurate in local scope yet suffers from drift in long distances. By contrast, camera relocalization performs well in the global sense but lacks local precision. We argue that these two tasks should be strategically combined to leverage the complementary advantages, and be further improved by exploiting the 3D geometric information from depth data, which is also beneficial for depth estimation in turn. Therefore, we present a collaborative learning framework, consisting of DepthNet, LocalPoseNet and GlobalPoseNet with a joint optimization loss to estimate depth, VO and camera localization unitedly. Moreover, the Geometric Attention Guidance Model is introduced to exploit the geometric relevance among three branches during learning. Extensive experiments demonstrate that the joint learning scheme is useful for all tasks and our method outperforms current state-of-the-art techniques in depth estimation and camera relocalization with highly competitive performance in VO.
Design of Blended Learning Based on Tri Kaya Parisudha Using Kelase Platform in Realizing Hybrid-Superitem Learning in Mathematics Lessons

This study aimed to develop the blended learning design using the Kelase platform with learning concepts that internalize the Tri Kaya Parisudha concept and the Mathematics material structure by using Superitem concept so that the Mathematics learning process at the level of high school or vocational can be more effective.This research was carried out with a development research approach using the Borg and Gall model with the main focus of development at the design development stage.Subjects involved in the development of the design were three researchers; the initial design trial was four experts and 30 Mathematics teachers.Data collection tools in the form of questionnaires, interview guidelines, and photo documentations.The technique used to analyze the data was quantitative descriptive.The results showed that the effectiveness of the blended learning design that had been developed was classified as good common with an average effectiveness percentage of 88.76%.Based on that effectiveness percentage was compared to the five-scale categorization, it's recommended not to do a major revision of the blended learning design.
Generalizing Graph Neural Networks Beyond Homophily.

We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Most existing GNNs fail to generalize to this setting, and are even outperformed by models that ignore the graph structure (e.g., multilayer perceptrons). Motivated by this limitation, we identify a set of key designs -- ego- and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations -- that boost learning from the graph structure under heterophily, and combine them into a new graph convolutional neural network, H2GCN. Going beyond the traditional benchmarks with strong homophily, our empirical analysis on synthetic and real networks shows that, thanks to the identified designs, H2GCN has consistently strong performance across the full spectrum of low-to-high homophily, unlike competitive prior models without them.
Pathology GAN: Learning deep representations of cancer tissue

Histopathological images of tumours contain abundant information about how tumours grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. In this paper, we develop a framework which allows Generative Adversarial Networks (GANs) to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on two different datasets, an H&E colorectal cancer tissue from the National Center for Tumor diseases (NCT, Germany) and an H&E breast cancer tissue from the Netherlands Cancer Institute (NKI, Netherlands) and Vancouver General Hospital (VGH, Canada). Composed of 86 slide images and 576 tissue micro-arrays (TMAs) respectively. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65 (breast cancer) and 32.05 (colorectal cancer). We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GANA video presentation of the work can be found here: https://youtu.be/RlBo4lAWtvM
P2L: Predicting Transfer Learning for Images and Semantic Relations

We describe an efficient method to accurately estimate the effectiveness of a previously trained deep learning model for use in a new learning task. We use this method, "Predict To Learn" (P2L), to predict the most likely "source" dataset to produce effective transfer for training on a "target" dataset. We validate our approach extensively across 21 tasks, including image classification tasks and semantic relationship prediction tasks in the linguistic domain. The P2L approach selects the best transfer learning model on 62% of the tasks, compared with a baseline of 48% of cases when using a heuristic of selecting the largest source dataset and 52% of cases when using a distance measure between source and target datasets. Further, our work results in an 8% reduction in error rate. Finally, we also show that a model trained from merging multiple source model datasets does not necessarily result in improved transfer learning. This suggests that performance of the target model depends upon the relative composition of the source dataset as well as their absolute scale, as measured by our novel method we term `P2L'.
Toward Real-world Panoramic Image Enhancement

Panoramic images captured by the fisheye lens cameras cover very wide field of view (FoV) ranging from 180° to 360°, but the image quality is very low compared to that of high-end cameras such as DSLR or compact cameras with APS-C or full frame sensors. In this paper, we aim to use deep neural network (DNN) based methods to improve panoramic image quality. Specifically, we enhance low quality panoramic images of 5K resolution (5376×2688) to high-end camera quality at the same resolution, which is good for applications that requires limited resources, low-cost but high image quality. We build a Panoramic-High-end dataset which is the first real world panoramic image dataset as far as we know. Based on the generative adversarial network (GAN) architecture, we also design a compact network employing multi-frequency structure with compressed Residual-in-Residual Dense Blocks (RRDBs) and convolution layers from each dense block. Experiments show that our method surpasses several state-of-the-art DNN based methods in both no-reference and full-reference evaluations as well as the processing speed. Our results show that it’s practical to integrate DNN based image enhancer into optics design to achieve a balance between optical cost and image quality.
Children Immunization App (CImA), Low-Cost Digital Solution for Supporting Syrian Refugees in Zaatari Camp in Jordan - General Description.

Mobile applications (apps) can improve health outcomes. In this study, we have described an app developed for documenting the history of vaccination among Syrian children in one of the largest refugees' camps in the Middle East region. This app includes health education information and automated reminders for parents, using a visual tool for parents with low literacy level. We have emphasized on the usability and technical concerns and have described the interdependency of technical and human considerations for such health app solution in a marginalized context.
Green and Quality Development of Service Industry in West Coast Economic Zone

Yin, L. and Zhang, X., 2020. Green and quality development of service industry in West Coast Economic Zone. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 1158–1161. Coconut Creek (Florida), ISSN 0749-0208.Thanks to the increasing-broadened scale of foreign investment, the orderly and green development of service industry in the West Coast Economic Zone has promoted the traditional service industry to emerge as the pillar of the tertiary industry in recent years. However, this zone also witnessed the poor internal structure of the service industry, non-quality employees, incomplete green development system of service industry, residents' weak consciousness of green production of enterprises and green consumption and loose green management, etc. In this regard, measures should be taken to optimize the internal structure of the service industry, create green growth points, and activate the market by developing green service industry agglomeration areas. In addition, it is necessary to ensure the elements of green development of service industry and deepen cooperation with Taiwan's service industry, so as to advance the green and high-quality development of service industry in the West Coast Economic Zone.
Multi Type Mean Field Reinforcement Learning

Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent. In this paper, we extend mean field multiagent algorithms to multiple types. The types enable the relaxation of a core assumption in mean field games, which is that all agents in the environment are playing almost similar strategies and have the same goal. We conduct experiments on three different testbeds for the field of many agent reinforcement learning, based on the standard MAgents framework. We consider two different kinds of mean field games: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations. We introduce new algorithms for each type of game and demonstrate their superior performance over state of the art algorithms that assume that all agents belong to the same type and other baseline algorithms in the MAgent framework.
AMD GPUs as an Alternative to NVIDIA for Supporting Real-Time Workloads

Graphics processing units (GPUs) manufactured by NVIDIA continue to dominate many fields of research, including real-time GPU-management. NVIDIA’s status as a key enabling technology for deep learning and image processing makes this unsurprising, especially when combined with the company’s push into embedded, safety-critical domains like autonomous driving. NVIDIA’s primary competitor, AMD, has received comparatively little attention, due in part to few embedded offerings and a lack of support from popular deep-learning toolkits. Recently, however, AMD’s ROCm (Radeon Open Compute) software platform was made available to address at least the second of these two issues, but is ROCm worth the attention of safety-critical software developers? In order to answer this question, this paper explores the features and pitfalls of AMD GPUs, focusing on contrasting details with NVIDIA’s GPU hardware and software. We argue that an open software stack such as ROCm may be able to provide much-needed flexibility and reproducibility in the context of real-time GPU research, where new algorithmic or analysis techniques should typically remain agnostic to the underlying GPU architecture. In support of this claim, we summarize how closed-source platforms have obstructed prior research using NVIDIA GPUs, and then demonstrate that AMD may be a viable alternative by modifying components of the ROCm software stack to implement spatial partitioning. Finally, we present a case study using the PyTorch deep-learning framework that demonstrates the impact such modifications can have on complex real-world software.
Advanced 3-Bit LSB Based on Data Hiding Using Steganography

Nowadays, security plays a main role for data hiding and makes it confidential from unauthorized access. So steganography and cryptography techniques are used for data security. In this paper, 3-bit least significant bits technique is used for embedding secrete image into the cover image. The resultant image after embedding the secrete image into cover image is a “Stego Image.” Using Vvisual Cryptography, transferring the stego image over the wireless channel or Internet becomes more securable because it divides the encrypted message and transfers it into two shares based threshold. In this paper, ECC algorithm is used to kept data more secure and secrete which makes very hard to get secrete message even if the secrete message is disclose. These proposed systems will work on large size of image and result with high performance and accuracy with high PSNR for color image.
How will Blockchain Technology Change Education Future?!

ackground and Aim of Study: The question “how” and “what to learn?” has always been urgent. New methodological approaches to education are appearing, pedagogical technologies are being worked out, innovative forms and methods of education are being implemented. The modern society being plunged into the information age, has not noticed that information technologies have started playing a more significant role in the educational process. A blockchain technology deserves a special attention nowadays. It appeared ten years ago and started its way as an undermining innovative technology that is moving upwards changing the old-fashioned forms. Having a great potential it has already started transforming financial and economic spheres, now it is turn for the sphere of education. The aim of the study: to turn the attention of scientific and pedagogical workers to possibilities of using the blockchain technology in the sphere of education, as well as characterise the peculiarities of its implementation.
Examining the relationship between social inclusion and mobile libraries in the age of Internet connectivity: A qualitative study of mobile librarians around the globe

In order to help serve community members suffering from public library access inequity, many library institutions provide mobile library services to help them connect directly with their communities and provide strong social, educational and emotional support to those in need. Via the use of a series of in-depth one-on-one interviews with individual mobile librarians practising in different parts of the world, this article aims to examine the new and changing roles of mobile libraries and librarians – the roles they play in supporting communities that suffer from a lack of readily accessible library resources, have extreme social inequity, or have lost touch with their usual library community owing to natural disasters. Nine mobile librarians took part in this study and the countries they represent are as follows: Australia, China, Croatia, Greece, Iceland, India, Japan, the USA and Zimbabwe. The user groups of the nine participating mobile libraries included refugees, impoverished children, migrant families and disaster-affected populations, who suffered, in particular, from extreme educational and informational inequality. The interview findings from the study indicate that mobile libraries play an important role in providing resources to different disadvantaged user groups, as well as enabling those with limited access to educational facilities and reading programmes with opportunities to survive and prosper in their communities. Not only do mobile libraries provide age- and content-appropriate resources and alternative literacy programmes for their patrons, but they also create new spaces for social connection for community members who may be disparate, isolated or physically unable to venture far from home. In summary, mobile libraries have the potential to function as a ‘social equaliser’ by extending the philosophy of social equality and erasing social, economic and educational barriers through the provision of literacy and learning opportunities for community members of all ages, abilities and backgrounds.