Reviewerly/exHarmony huggingface dataset:
SocialRec: A Context-Aware Recommendation Framework With Explicit Sentiment Analysis

In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Usually, the recommendation systems match users' preferences based on the star ratings provided by the users for various products. However, simply relying on users' ratings about an item can produce biased opinions, as a user's textual feedback may differ from the item rating provided by the user. In this paper, we propose SocialRec, a hybrid context-aware recommendation framework that utilizes a rating inference approach to incorporate users' textual reviews into traditional collaborative filtering methods for personalized recommendations of various items. We apply text-mining algorithms on a large-scale user-item feedback dataset to compute the sentiment scores. We propose a greedy heuristic to produce ranking of items based on users' social similarities and matching preferences. To address challenges resulting from cold start and data sparseness, SocialRec introduces pre-computation models based on Hub-Average (HA) inference. Rigorous evaluations of SocialRec (on large-scale datasets) demonstrate high accuracy, especially in comparison with previous related frameworks.
The Arboricity Captures the Complexity of Sampling Edges

In this paper, we revisit the problem of sampling edges in an unknown graph $G = (V, E)$ from a distribution that is (pointwise) almost uniform over $E$. We consider the case where there is some a priori upper bound on the arboriciy of $G$. Given query access to a graph $G$ over $n$ vertices and of average degree $d$ and arboricity at most $\alpha$, we design an algorithm that performs $O\!\left(\frac{\alpha}{d} \cdot \frac{\log^3 n}{\varepsilon}\right)$ queries in expectation and returns an edge in the graph such that every edge $e \in E$ is sampled with probability $(1 \pm \varepsilon)/m$. The algorithm performs two types of queries: degree queries and neighbor queries. We show that the upper bound is tight (up to poly-logarithmic factors and the dependence in $\varepsilon$), as $\Omega\!\left(\frac{\alpha}{d} \right)$ queries are necessary for the easier task of sampling edges from any distribution over $E$ that is close to uniform in total variational distance. We also prove that even if $G$ is a tree (i.e., $\alpha = 1$ so that $\frac{\alpha}{d}=\Theta(1)$), $\Omega\left(\frac{\log n}{\log\log n}\right)$ queries are necessary to sample an edge from any distribution that is pointwise close to uniform, thus establishing that a $\mathrm{poly}(\log n)$ factor is necessary for constant $\alpha$. Finally we show how our algorithm can be applied to obtain a new result on approximately counting subgraphs, based on the recent work of Assadi, Kapralov, and Khanna (ITCS, 2019).
WristPress: Hand Gesture Classification with two-array Wrist-Mounted pressure sensors

This paper presents a hand gesture recognition system WristPress based on only the pressure sensors, which can reflect the different pressure changes of different hand gestures. Two arrays of force sensitive resistors (FSRs) are arranged around the wrist to capture the pressure fluctuation with the subtle muscle and tendon movements of different gestures, which can help to identify similar gestures for achieving more functions. For distinguishing more gestures with similar muscle and tendon movements, the temporal features and the spatial features of pressures are selected and designed to characterize the relation of every tiny pressure changes corresponding to the muscle and tendon movements at different positions around the wrist. In the WristPress system, 24 kinds of one-gestures, which cover not only the finger movements but also rotations around the wrist and forearm, are classified with an overall 10-fold cross validation classification accuracy of 97.40%. In addition, the WristPress prototype is non-obtrusive with a small size, and is well suited to existing wearable device forms, such as smart watches and a bracelet that are already mounted on the wrist. Our study shows that the temporal features and the spatial features of these pressures can reflect the the correlation between different pressure sensors can improve the accuracy of the hand gesture classification, and the kNN classifier has the best classification accuracy performance 97.40% with a low time complexity.
Second International Workshop on Variability and Evolution of Software-Intensive Systems (VariVolution 2019)

Most of today's software systems evolve continuously and need to exist in various variants to address different requirements. However, changes resulting from evolution in time (i.e., revisions) and changes resulting from evolution in space (i.e., variants) are managed completely differently. In particular, no traditional technology for version management provides convenient means to effectively and efficiently support unified revision and variant management. Researchers from various communities have proposed new concepts and techniques to tackle this problem. Especially the communities of software configuration management, software product lines, and software versioning are working on such a unified technology. For example, variation control systems have been proposed to systematically manage revisions and variants based on a unified perspective of evolution in time and space. VariVolution (the 2nd International Workshop on Variability and Evolution of Software-Intensive Systems) aims to gather researchers and practitioners that are working on or interested in software evolution and variability. The workshop offers an opportunity to exchange ideas, report real-world cases and problems, and initiate new research directions and collaborations.
QuGAN: Quasi Generative Adversarial Network for Tibetan Question Answering Corpus Generation

In recent years, the large-scale open Chinese and English question answering (QA) corpora have provided important support for the application of deep learning in the Chinese and English QA systems.However, for low-resource languages, such as Tibetan, it is difficult to construct satisfactory QA systems, owing to the lack of large-scale Tibetan QA corpora.To solve this problem, this paper proposes a QA corpus generation model, called QuGAN.This model combines Quasi-Recurrent Neural Networks and Reinforcement Learning.The Quasi-Recurrent Neural Networks model is used as a generator for Generative Adversarial Network, which speeds up the generation of text.At the same time, the reward strategy and Monte Carlo search strategy are optimized to effectively update the generator network.Finally, we use the Bidirectional Encoder Representations from Transformers model to correct the generated questions at the grammatical level.The experimental results show that our model can generate a certain amount of effective Tibetan QA corpus, and the BLEU-2 value increases by 13.07%than baseline.Moreover, the speed of the model has been greatly improved.
Electric Device Abnormal Detection Based on IoT and Knowledge Graph

Abnormal detection is an essential task to the security and stability of power grid networking operation. Besides the status of power grid itself, the environment information is also necessary to the abnormal detection, such as temperature, humidity, dust and etc. Therefore, we apply the Internet of Things (IoT) technology to monitor the real time status of power grid and its related environment. Then a knowledge based method is proposed to find out the reference data for the abnormal detection and signal correlation algorithm is applied to calculate if the given IoT data is abnormal. The experiment results indicate that the proposed method can integrate the data from multiple resources and perform an accuracy analysis of the abnormality of the given IoT data. The overall accuracy improves from 64% with tradition threshold based method to 92% according to the test dataset.
Measurement of Dynamic Responses from Large Structural Tests by Analyzing Non-Synchronized Videos

Image analysis techniques have been employed to measure displacements, deformation, crack propagation, and structural health monitoring. With the rapid development and wide application of digital imaging technology, consumer digital cameras are commonly used for making such measurements because of their satisfactory imaging resolution, video recording capability, and relatively low cost. However, three-dimensional dynamic response monitoring and measurement on large-scale structures pose challenges of camera calibration and synchronization to image analysis. Without satisfactory camera position and orientation obtained from calibration and well-synchronized imaging, significant errors would occur in the dynamic responses during image analysis and stereo triangulation. This paper introduces two camera calibration approaches that are suitable for large-scale structural experiments, as well as a synchronization method to estimate the time difference between two cameras and further minimize the error of stereo triangulation. Two structural experiments are used to verify the calibration approaches and the synchronization method to acquire dynamic responses. The results demonstrate the performance and accuracy improvement by using the proposed methods.
An Innovative Damped Cuckoo Search Algorithm With a Comparative Study Against Other Adaptive Variants

This paper aims to find the best variant of Cuckoo Search Algorithm that makes the step size of Lévy flight adaptive. For this reason, we introduce a new variant of CSA called Damped Cuckoo Search (DCS) in which the step size of Lévy flight is adaptive via the concept of damped oscillations that exist in most second order control systems. Moreover, we propose two other methods that tune the step size of Lévy flight based on chaotic maps. Then a deep comparative study is conducted among almost all variants of CSA that appeared in the last decade that modifies the step size of Lévy flight. All these variants are tested on CEC2017 benchmark functions. Statistical analyses are performed using the Friedman test followed by four post-hoc procedures to hold paired comparisons between the proposed DCS and the other CSA variants. Also, graphical statistical analyses are conducted on all variants via Box Plots. Finally, convergence graphs for all the variants are illustrated as well to show the speed of solution improvement over generations. Simulation results prove that the proposed DCS outperforms all other variants with a large degree of significance. Moreover, DCS increases the speed of convergence in comparison with the other variants. The box plot graphs prove that DCS has the most compact distribution for all results obtained in all runs on most functions.
Haze Removal Using Aggregated Resolution Convolution Network

The haze removal technique refers to the process of reconstructing haze-free images from scenes of inclement weather conditions. This task has an extensive demand in practical applications. At present, models based on deep convolution neural networks have made significant progress in the haze removal field, greatly outperforming the traditional prior and constraint methods. However, the current CNNs methods, which involve only a single input image, do not provide sufficient features to determine the optimal transmission maps for haze removal; therefore, we propose and design an aggregated resolution convolution network (ARCN) that uses multiple inputs and aggregates features from a CNN model and the adversarial loss algorithm. Experiments comparing the visual results of our network with those of several previous methods reveal substantial improvements.
Progressive Pose Normalization Generative Adversarial Network for Frontal Face Synthesis and Face Recognition Under Large Pose

This paper proposes a Progressive Pose-Normalization Generative Adversarial Network (PPN-GAN) for frontal face synthesis and face recognition. The key idea is to normalize a profile face progressively: starting from inferring an intermediate face that has a small view difference to the profile face, and then increasing the view difference step by step, until the frontal view of the profile face is recovered. In addition to the progressive strategy, an additional identity discriminator and identity-aware losses in both the image and feature spaces are also incorporated into the GAN for identity preserving. Experimental results show that our method not only produces compelling perceptual results but also outperforms the state-of-the-art methods on face recognition under large-pose.
Neural Machine Translation with Soft Prototype

Neural machine translation models usually use the encoder-decoder framework and generate translation from left to right (or right to left) without fully utilizing the target-side global information. A few recent approaches seek to exploit the global information through two-pass decoding, yet have limitations in translation quality and model efficiency. In this work, we propose a new framework that introduces a soft prototype into the encoder-decoder architecture, which allows the decoder to have indirect access to both past and future information, such that each target word can be generated based on the better global understanding. We further provide an efficient and effective method to generate the prototype. Empirical studies on various neural machine translation tasks show that our approach brings significant improvement in generation quality over the baseline model, with little extra cost in storage and inference time, demonstrating the effectiveness of our proposed framework. Specially, we achieve state-of-the-art results on WMT2014, 2015 and 2017 English to German translation.
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling

In this paper we propose to use a denoising autoencoder (DAE) prior to simultaneously solve a linear inverse problem and estimate its noise parameter. Existing DAE-based methods estimate the noise parameter empirically or treat it as a tunable hyper-parameter. We instead propose autoencoder guided EM, a probabilistically sound framework that performs Bayesian inference with intractable deep priors. We show that efficient posterior sampling from the DAE can be achieved via Metropolis-Hastings, which allows the Monte Carlo EM algorithm to be used. We demonstrate competitive results for signal denoising, image deblurring and image devignetting. Our method is an example of combining the representation power of deep learning with uncertainty quantification from Bayesian statistics.
Virtual Reality Video Quality Assessment Based on 3d Convolutional Neural Networks

As a new medium, Virtual Reality (VR) has attracted widespread attentions and research interests. More and more researchers have built their VR image/video database and devise related algorithms. However, the existing methods of VR video quality assessment are not very effective, and one of the most important reasons is that the database is not suitable. To this end, this paper proposes an efficient VR quality assessment method on self-built database. Firstly, we establish a VR video quality assessment database with subjective scores, and add the projection format to the production of the database. Secondly, the database is proved to be valid by using some traditional image quality assessment metrics. Lastly, we design a 3D convolutional neural network to predict the VR video quality without reference VR video. Meanwhile, taking the pre-processed VR video patches as input, different quality score strategy is applied to get the final score. The experimental results surface that the network we designed has good results, and the performance is improved after the weight calculation combined with the projection format.
PELATIHAN PENULISAN ARTIKEL UNTUK PUBLIKASI DI JURNAL ILMIAH UNTUK MENINGKATKAN PROFESIONALISME BAGI GURU-GURU DI KECAMATAN KUBUTAMBAHAN KABUPATEN BULELENG

PELATIHAN PENULISAN ARTIKEL UNTUK PUBLIKASI DI JURNAL ILMIAHUNTUK MENINGKATKAN PROFESIONALISME BAGI GURU-GURU DIKECAMATAN KUBUTAMBAHAN KABUPATEN BULELENGOlehKetut Sedana ArtaJurusan Pendidikan SejarahABSTRACTThe main objectives of the Community Service activities are: (1) Enhancing thedevelopment of teachers' professionalism in the context of promotion requirements, (2)Improving the ability of teachers to compile and present scientific papers, (3) Produceproducts of scientific work that are ready to be published in scientific journalsThe results obtained from all stages of the P2M implementation activities can be detailed asfollows: teachers at every level the ability to write scientific articles is still lacking. Someobservations and interviews with teachers provide clarity as to why teachers have not beenable, willing, and used to writing science. The causes of the low ability of teachers in writingscientific papers, namely: (1) lack of knowledge, understanding, and skills of teachers inwriting scientific papers, especially writing scientific articles, (2) limited scientific readingfacilities, especially in the form of scientific magazines or journals, (3) the unavailability ofmagazines or journals in the school or district education office that can accommodate thewritings of the teachers, (4) the limited implementation of scientific writing competitions heldby the education office at the national, provincial and district levels, and (5) the lowmotivation of teachers to take part in scientific writing competitions. In connection with this,there are several strategies offered, one of them is by conducting article training forpublication in a journal. After being given training by a team of experts from UndikshaSingaraja, elementary school teachers can begin to compile and package articles.Keywords: write, articles, published, scientific, journals
Deep Compression: A Compression Technology for Apron Surveillance Video

This paper presents a deep compression method that uses some object detection methods to separate moving and stationary objects from a real frame in an apron surveillance video. The extracted object image, the corresponding position information and the background image are stored in a linked list. When the video is decompressed, the extracted object images are restored to the background image according to the corresponding information, and the overall adjustment, such as illumination, is performed according to the stored information. Finally, a video with high similarity to the original video is generated. This video compression method greatly reduces the required storage space without destroying the original video information.
Multi-Objective Materialized View Selection Using Improved Strength Pareto Evolutionary Algorithm

A data warehouse system uses materialized views extensively in order to speedily tackle analytical queries. Considering that all possible views cannot be materialized due to maintenance cost and storage constraints, the selection of an appropriate set of views to materialize that achieve an optimal trade-off among query response time, maintenance cost, and the storage constraint becomes an essential necessity. The selection of such an appropriate set of views for materialization is referred to as the materialized views selection problem, which is an NP-Complete problem. In the last two decades, several new selection approaches, based on heuristics, have been proposed. Most of these have used a single objective or weighted sum approach to address the various constraints. In this article, an attempt has been made to address the bi-objective materialized view selection problem, where the objective is to minimize the view evaluation cost of materialized views and the view evaluation cost of the non-materialized views, using the Improved Strength Pareto Evolutionary Algorithm. The experimental results show that the proposed multi-objective view selection algorithm is able to select the Top-K views that achieves a reasonable trade-off between the two objectives. Materializing these selected views would reduce the query response times for analytical queries and thereby facilitates the decision-making process.
Optimization of ISP parameters for object detection algorithms

In autonomous driving applications, cameras are a vital sensor as they can provide structural, semantic and navigational information about the environment of the vehicle. While image quality is a concept well understood for human viewing applications, its definition for computer vision is not well defined. This gives rise to the fact that, for systems in which human viewing and computer vision are both outputs of one video stream, historically the subjective experience for human viewing dominates over computer vision performance when it comes to tuning the image signal processor. However, the rise in prominence of autonomous driving and computer vision brings to the fore research in the area of the impact of image quality in camera-based applications. In this paper, we provide results quantifying the accuracy impact of sharpening and contrast on two image feature registration algorithms and pedestrian detection. We obtain encouraging results to illustrate the merits of tuning image signal processor parameters for vision algorithms.
No More Free Riders: Sharing WiFi Secrets with Acoustic Signals

Shopping malls, restaurants, and book shops usually offer free WiFi to customers, but they cannot afford non-customers (i.e., WiFi free riders) consuming their limited bandwidth. To provide WiFi information (i.e., SSID and password) secretly to customers, they usually display it in places where only customers can see, and update it regularly. The drawbacks are that customers need to enter the password manually and sometimes they do not know the location of the information. In this paper, we propose an alternative solution. In our system, the WiFi providers set up a speaker to continuously broadcast acoustic signals encoding WiFi information, and the customers use their smartphones to receive and decode the signals. Because acoustic signals attenuate rapidly through walls, only customers in the venue can acquire WiFi information. We implement a prototype system and show the feasibility of our approach. Experiments indicate that in our system, a user can connect successfully to the WiFi within several seconds.
An Effective Algorithm and Architecture for the High-Throughput Lossless Compression of High-Resolution Images

This paper proposes a high-throughput lossless image-compression algorithm based on Golomb-Rice coding and its hardware architecture. The proposed solution increases compression ratios (CRs) while preserving the throughput by taking advantage of a novel parallel variable-length sign coding (PVSC) algorithm that reduces the sign bits to achieve a higher CR. In addition, the proposed solution adopts and modifies the two existing compression algorithms to improve the overall compression performance. The experimental results show that the proposed solution yields an average CR of 3.12, which is higher than those achieved with the previous algorithms. The hardware implementation of the proposed solution for an 8x8 block unit achieves a throughput of 18 GBps and 24 GBps when encoding and decoding, respectively. This hardware performance is enough to handle 7680 x 4320@240-Hz image processing.