Reviewerly/exHarmony huggingface dataset:
Group oriented trust-aware location recommendation for location-based social networks

With the increasing popularity of social networks and online communities, group recommendation systems arise in order to support users to interact with those having similar interests, and to provide recommendations for joint activities, such as eating out as a group or seeing a movie with friends. However, the techniques and approaches to provide recommendations to groups are limited, as most of the available studies focus on individual recommendations. In this study, we address the problem of recommending venues to a group of users by employing Random Walk with Restart (RWR) algorithm to generate recommendations based on the current location of group members, experts and trusted users visiting the same venues. We propose a new approach by including the trust factor of users in location-based social networks (LBSNs). The first one aggregates the location recommendations that are generated with the Random Walk algorithm for each member in the group, taking the preferences and objectivity scores of the individuals into account. The second one is based on creating a group profile by blending preferences and venue category types, and using this group profile to run the Random Walk algorithm once. Comprehensive experiments have been performed on different group sizes, and including trust factor of users. The analysis is conducted on the data collected from the location based social network platform Foursquare. The experiments have shown that the trust factor of users improves the performance of group recommendation system and the proposed algorithm provides recommendations to groups with high accuracy compared to the baselines.
Making distributed edge machine learning for resource-constrained communities and environments smarter: contexts and challenges

Abstract The maturity of machine learning (ML) development and the decreasing deployment cost of capable edge devices have proliferated the development and deployment of edge ML solutions for critical IoT-based business applications. The combination of edge computing and ML not only addresses the development cost barrier, but also solves the obstacles due to the lack of powerful cloud data centers. However, not only the edge ML research and development is still at an early stage and requires substantial skills normally missed in resource-constrained communities, but also various infrastructure constraints w.r.t. network reliability and computing power, and business contexts from the resource-constrained environments require different considerations to make edge ML applications context aware through smart and intelligent runtime strategies. In this paper, we analyze representative real-world business scenarios for edge ML solutions and their contexts in resource-constrained communities and environments. We identify and map the key distinguished contexts of distributed edge ML and discuss the impacts of these contexts on data and software components and deployment models. Finally, we present key research areas, how we should approach them, and possible tooling for making edge machine learning solutions smarter in resource-constrained communities and environments.
Human Visual System Guided Reversible Data Hiding Based On Multiple Histograms Modification

Abstract In this paper, we propose a human visual system (HVS) guided reversible data hiding method based on multiple histograms modification. The proposed method utilizes the texture features of an image to adaptively modify the pixels, for a lower HVS quality distortion. The HVS quality is taken as the optimization objective and a new expansion-bin-selection strategy is given to solve the optimization. For the same HVS quality distortion, the proposed method can embed more secret bits and give higher priority for the modifications in texture regions. Besides, a better optimization rule is proposed to accelerate the speed and then consider more available solutions. In this way, a trade-off between the embedding performance and time complexity can be achieved. Experimental results show that the proposed method can achieve a better HVS quality than the conventional methods.
Comparing Annotation Types and <i>n</i>-Gram Sizes

The chapter addresses a problem of contrastive pragmatics: How can we study correspondences between pragmatic markers in two languages if one language has a class of elements that the other language lacks? Specifically, the contribution deals with modal particles of German (ja and doch) and their reflexes in English translations. As there is no predetermined set of potential English correspondences, traditional distributional analyses are not feasible, and methods from Natural Language Processing are explored instead. Using 32 types of n-grams, differing in length and type of annotation, three classification tasks are carried out, in order to identify cues in the English translations that reflect the presence (or absence) of a particle in the German original. The results show that lemma-unigrams and -bigrams are often most informative (i.e. most accurate), while trigrams and 1-skip-2-grams provide important information about concomitants of modal particles that unigrams and bigrams miss. The results show that linguistic observables (n-grams) as the basis of quantitative analyses need to be carefully selected and explored in terms of their contribution to linguistic analysis.
Disruption of Library Services Due to Hospital Cyberattack: A Case Study

Cyberattacks on healthcare organizations increased dramatically in 2020 and 2021. The University of Vermont Medical Center suffered an attack in October 2020, during the second wave of the COVID-19 pandemic. The disruption to hospital computer systems had wide-ranging impacts, including loss of online access to the medical library for nearly three months. Library staff worked to reduce impacts and increase access for hospital employees until full access was restored. This case study offers lessons learned and resources for health sciences libraries planning for a potential cyberattack.
A motion-based approach for real-time detection of pornographic content in videos

In recent years, several works have proposed highly accurate CNN-based pornography video detection approaches. However, current techniques are unable to cope with the context-dependent nature of pornography content, wherein the analyzed video frame class may change according to its context, whether it is pornographic related or not. This paper proposes a motion-based approach for fine-grained real-time detection of pornographic content in videos implemented in two phases. First, we extract and classify motion-based descriptors built over adjacent video frames to build a motion image from the analyzed video frame. Second, we jointly evaluate the outcome of each single classified motion descriptor to produce a final video frame classification. Experiments performed in a novel fine-grained dataset built from the manual analysis of over 400 thousand video frames, show that current approaches in the literature are unable to cope with context-dependent pornographic content. In contrast, our proposal can maintain the system accuracy, even in the presence of context-dependent pornographic content, hence, maintaining its reliability. In addition, when the extracted motion-based descriptors are jointly evaluated, our proposal is able to improve the detection accuracy by up to 29%.
Verifying redundant-check based countermeasures

To thwart fault injection based attacks on critical embedded systems, designers of sensitive software use redundancy based countermeasure schemes. In some of these schemes, critical checks (i.e. conditionals) in the code are duplicated to ensure that an attacker cannot bypass such a check by flipping its result in order to get to a protected point (corresponding e.g. to a successful authentication or code integrity verification). This short paper presents a source-code-level verification technique of the correct implementation of such countermeasures. It is based on code instrumentation and deductive verification. The proposed technique was implemented in a tool prototype and evaluated on a real-life case study: the bootloader module of a secure USB storage device called WooKey, supposed to be resistant to fault injection attacks. We were able to prove the correctness of almost all redundant-check countermeasures in the module except two, and found an error in one of the unproven ones.
Designing a Workflow for Online Learning Objects Created by Health Sciences Librarians

The COVID-19 lockdown led to immediate changes in how Virginia Commonwealth University's (VCU) Health Sciences Library (HSL) would support faculty and students through the means of online learning objects (OLOs). Each Research and Education (RED) librarian is responsible for responding to the educational needs of a specific health sciences school or college as well as those of the VCU Health System. A rapid increase in the OLO creation required a mechanism to curate these objects, make them available to all liaisons, and standardize workflows. The act of curating and creating standardized workflows would allow for easier management and updating of content, the ability to share and cross-pollinate content between liaisons, and the prevention of duplicated content by liaisons, thus lessening the workload. Support from key stakeholders, including RED administrators, the Online Learning Librarian (OLL), and the Multimedia Teaching and Learning Librarian (MTLL), enabled a team of RED librarians (who formed an Online Learning Team (OLT)) to standardize workflows and upload them to the department's intranet for future reference.
A Story by Academic Teachers About Distance Education in the Time of Lockdown

This article was written as part of an academic project about the autoethnographs of the times of crisis that academic teachers from several universities in Poland took part in. From the collected stories about experiencing the pandemic, we singled out the thread of distance learning. We created a multivoiced narrative and then submitted it to a collective reflection. Collective autoethnography made it possible not only to recognize changes in the thinking about the self, academic culture, and teaching itself but also to pose questions about the possibility of transformation.
CCFR3: A cooperative co-evolution with efficient resource allocation for large-scale global optimization

Cooperative co-evolution (CC) adopts the divide-and-conquer strategy to decompose an optimization problem, which can decrease the difficulty of solving large-scale optimization problems. Each decomposed subproblem is solved by a subpopulation. According to the contributions of the subpopulations to the improvement of the best overall objective value, the CC algorithms select the subpopulation with the greatest contribution to undergo evolution. In the existing CC algorithms, the contribution evaluation cannot adapt to solve the optimization problem, which may decrease the performance of CC. In this paper, we propose a new CC framework named CCFR3, which can adaptively evaluate the contribution of a subpopulation in each co-evolutionary cycle. CCFR3 can allocate computational resources among subpopulations more frequently than other contribution-based CC algorithms. The subpopulations can have more chances to undergo evolution, which is beneficial to speed up the convergence of CC and enhance the performance of CC on obtaining the global optimal solution. Our experimental results and analysis suggest that CCFR3 is a competitive solver for large-scale optimization problems.
Towards privacy aware deep learning for embedded systems

Memorization of training data by deep neural networks enables an adversary to mount successful membership inference attacks. Here, an adversary with blackbox query access to the model can infer whether an individual's data record was part of the model's sensitive training data using only the output predictions. This violates the data confidentiality, by inferring samples from proprietary training data, and privacy of the individual whose sensitive record was used to train the model. This privacy threat is profound in commercial embedded systems with on-device processing. Addressing this problem requires neural networks to be inherently private by design while conforming to the memory, power and computation constraints of embedded systems. This is lacking in literature.
ReactiveMP.jl: A Julia package for reactive variational Bayesian inference

Variational Bayesian (VB) inference has become an increasingly popular method for approximating exact Bayesian inference in model-based machine learning. The VB approach provides a way to trade off accuracy versus computational complexity and scales better to large-dimensional inference problems than sampling solutions. The Julia package ReactiveMP.jl implements and automates reactive VB inference by minimization of a constrained Bethe Free Energy functional through message passing on a factor graph representation of a probabilistic model. Moreover, through support for specification of explicit constraints on the Free Energy functional, ReactiveMP.jl allows for comparative analysis of different variational cost function proposals.
A Comparative Analysis of Solar PV Forecast using SVM under CO2 Concentration

Installation of a solar PV plant requires an understanding of the performance of solar PV panels in various weather conditions by which solar output power has to be predicted well in advance. Solar PV technology is the most reliable and cost-effective technology compared to other renewable energy technologies. To minimize the cost of production and pollution, it is very essential to improve the techno-economic parameters of the technology and have a better understanding of the development of solar PV technology but the efficiency of solar PV technology depends on various environmental factors. Irradiation and temperature are the main inputs in solar PV technology. Again both the terms depend on greenhouse content and its concentration in the atmosphere. Due to the influence of many factors, the forecasting of the solar PV performance in terms of output is a difficult task. So various comparative analysis has been used for forecasting solar PV power. This paper has analysed support vector machines through the MATLAB simulation model for forecasting the performance parameters of the solar PV system. An experiment has been conducted at NRRI, Cuttack in collecting real-time data for analysis.
On the Role of Smart Vision Sensors in Energy-Efficient Computer Vision at the Edge

The increasing focus of the research community towards lightweight and small footprint neural network models is closing the gap between inference performance in cluster-scale models and tiny devices. In the recent past, researchers have shown how it is possible to achieve state-of-the-art performance in different domains (e.g. sound event detection, object detection, image classification) with small footprints and low computational cost architectures. However, these studies lack a comprehensive analysis of the input space used (e.g. for images) and present the results on standard RGB benchmarks. In this manuscript, we investigate the role of smart vision sensors (SVSs) in deep learning-based object detection pipelines. In particular, we combine the motion bitmaps with standard color spaces representations (namely, RGB, YUV, and grayscale) and show how SVSs can be used optimally for an IoT end-node. In conclusion, we report that, overall, the best-performing input space is grayscale augmented with the motion bitmap. These results are promising for real-world applications since many SVSs provide both image formats at low power consumption.
Restarted randomized surrounding methods for solving large linear equations

A class of restarted randomized surrounding methods are presented to accelerate the surrounding algorithms by restarted techniques for solving the linear equations. Theoretical analysis shows that the proposed method converges under the randomized row selection rule and the convergence rate in expectation is also addressed. Numerical experiments further demonstrate that the proposed algorithms are efficient and outperform the existing method for overdetermined and underdetermined linear equations, as well as in the application of image processing.
Development of a GAN architecture based on integrating global and local information for paired and unpaired medical image translation

The purpose of this study is to develop and evaluate a novel end-to-end generative adversarial network for image-to-image translation of medical paired and unpaired data. The proposed deep framework is based on generative adversarial networks, in which blocks are designed and used to improve the estimated images and achieve more accurate results. A joint attention block is designed to capture and fuse the global and local information during the training process. A dual discriminator is employed to accurately determine whether the generated images are real or fake. A global guidance up-sampling block is proposed to reconstruct the local details of the estimated images with guidance of the low-level information of the shallow layers. Moreover, an edge detection network is utilized to make the tissue edges sharper. The loss function is composed of different criteria considering the geometric characteristics for obtaining more accurate results. Different translation tasks including PD/T2, CT/MR, and PET/CT are considered to evaluate the performance of the proposed deep framework. The evaluation results indicated averagely 28.28, 10.87, 31.30, 0.90, 0.92, 0.85, 24.80, and 0.042 for root-mean-squared error (RMSE), mean absolute error (MAE), peak signal to noise ratio (PSNR), dice similarity coefficient (DSC), normalized cross-correlation (NCC), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS) and frechet inception distance (FID) respectively. Evaluation of the results showed improvement of 106, 66, 12, 0.22, 0.17, 0.15, 58, and 0.39 for RMSE, MAE, PSNR, DSC, NCC, SSIM, LPIPS, and FID, respectively when the proposed blocks were used. Also, comparing the performance of the proposed network against other methods in the two modes of using paired and unpaired data showed the better performance of the proposed network.
Stereo-consistent screen-space ambient occlusion

Screen-space ambient occlusion (SSAO) shows high efficiency and is widely used in real-time 3D applications. However, using SSAO algorithms in stereo rendering can lead to inconsistencies due to the differences in the screen-space information captured by the left and right eye. This will affect the perception of the scene and may be a source of viewer discomfort. In this paper, we show that the raw obscurance estimation part and subsequent filtering are both sources of inconsistencies. We developed a screen-space method involving both views in conjunction, leading to a stereo-aware raw obscurance estimation method and a stereo-aware bilateral filter. The results show that our method reduces stereo inconsistencies to a level comparable to geometry-based AO solutions, while maintaining the performance benefits of a screen-space approach.
Enabling On-Device Smartphone GPU based Training: Lessons Learned

Deep Learning (DL) has shown impressive performance in many mobile applications. Most existing works have focused on reducing the computational and resource overheads of running Deep Neural Networks (DNN) inference on resource-constrained mobile devices. However, the other aspect of DNN operations, i.e. training (forward and backward passes) on smartphone GPUs, has received little attention thus far. To this end, we conduct an initial analysis to examine the feasibility of on-device training on smartphones using mobile GPUs. We first employ the open-source mobile DL framework (MNN) and its OpenCL backend for running compute kernels on GPUs. Next, we observed that training on CPUs is much faster than on GPUs and identified two possible bottlenecks related to this observation: (i) computation and (ii) memory bottlenecks. To solve the computation bottleneck, we optimize the OpenCL backend&#x2019;s kernels, showing 2x improvements (40-70 GFLOPs) over CPUs (15-30 GFLOPs) on the Snapdragon 8 series processors. However, we find that the full DNN training is still much slower on GPUs than on CPUs, indicating that memory bottleneck plays a significant role in the lower performance of GPU over CPU. The data movement takes almost 91% of training time due to the low bandwidth. Lastly, based on the findings and failures during our investigation, we present limitations and practical guidelines for future directions.
At-scale assessment of weight clustering for energy-efficient object detection accelerators

DNN-based object detection operates on large data volumes to fetch images and DNN weights, which leads to high power and bandwidth demands. Solutions to mitigate those demands, such as weight clustering, are normally studied in limited examples of a much smaller scale than target applications, which poses difficulties to determine the best tradeoff to implement. This paper performs an at-scale (using a real life application) assessment of weight clustering for a DNN-based object detection system - You Only Look Once (YOLO) - considering real driving videos. Our case study shows that an Output Stationary accelerator (e.g. a systolic array) restricting weights to only between 32 (5-bit) and 256 (8-bit) different values allows preserving the accuracy of the original 32-bit weights of YOLO while decreasing bandwidth requirements to around 30%-40% of the original bandwidth, and overall energy consumption to around 45% of the original consumption. Overall, our case study provides key insights on which to take design decisions for an accelerator for camera-based object detection.
Compact graph neural network models for node classification

Recent research on graph convolutional networks tend to increase the complexity and non-linearity of graph convolution operators. Many of these operators result in models exploiting a huge number of learnable parameters, that have to be tuned during the training phase. This aspect makes the application of these approaches on huge datasets challenging due to the considerable computational time required by the training phase. On the other hand, having a huge number of parameters limits the applicability of the models in many node classification problems, in particular in the semi-supervised setting where the number of samples for which the target is known is limited to a few dozen nodes.
v-TCM

Understanding 1 and predicting user click behavior on a web search engine results page is critical for online advertising and recommendation engines. The click prediction results can be further used to estimate the relevance of the search engine results. Sequence-to-sequence models are effective for predicting user click behavior since they consider contextual information in the input sequence, and transformers are the recent state of the art in sequence learning. A novel transformer model is proposed in this paper for click prediction and relevance estimation that learns additionally from the vertical information, apart from the query and search engine results that are the inputs for the traditional click models. Vertical information is the representation style of a document in the search engine results page. The proposed vertical-aware transformer click model (v-TCM) takes into account both the position bias and the vertical bias of the search engine results. From our experiments, it is proved that v-TCM outperforms the existing click models for the click prediction and relevance estimation tasks.