Reviewerly/exHarmony huggingface dataset:
Poster

While containerization has emerged as a lightweight approach to package, deploy, and run legacy applications in a resource-efficient manner, the shared kernel-resource model used by containers introduces critical security concerns. Specifically, the abuse of system calls by a compromised container can trigger the security vulnerabilities of a host kernel. Unfortunately, even though existing solutions provide powerful protection mechanisms against such issues, how to define the capabilities of containers is still up to operators. In this work, we thus introduce TCLP, a dynamic analysis system that helps operators configure the least capabilities of containers to protect not only themselves but also a host. TCLP monitors the system calls triggered by containers in run time and finds the least capabilities required to run the containers based on the collected system calls. Finally, operators configure the minimal capabilities discovered by TCLP for their containers, reducing the risk of kernel vulnerabilities.
Exposure Correction and Local Enhancement for Backlit Image Restoration

Backlighting is a poor illumination condition where the primary light source illuminates a part of the scene from behind. While the part of the scene (often termed as backlit) suffers from low lighting condition, rest of the scene is either well-exposed or over-exposed. We aims to restore such images through enhancement using exposure correction. We generate pseudo images based on the relation of exposure with aperture and shutter speed in a camera. Human visual system (HVS)-sensitive and spatial frequency-aware multi-scale fusion is carried out for exposure correction to produce a globally enhanced image from the input and the pseudo images. Following this, we locally enhance the globally enhanced image to incorporate the information of frequently appearing intensity differences in a spatial neighborhood. Experimental results show that our technique outperforms other relevant approaches subjectively. Quantitative evaluation in terms of DE, EME, PixDist, LOE, AMBE measures shows the superiority of our technique over the other techniques. Our technique is faster than the approaches compared here while generating enhanced and naturalness preserved outputs.
Poster

Facebook remains the largest social media platform on the Internet with over one billion active monthly users. A variety of personal and sensitive data is shared on the platform, which makes it a prime target for attackers. Increasingly, we see phishing attacks that take advantage of users' lack of security knowledge, deceiving victims by using fake or compromised accounts to share malicious posts. These attacks may slip undetected by the Facebook defense system, exposing users to potentially be phished or have their devices infected with drive-by downloads and malware. Only a few studies have been conducted to date to understand how users interact with attacks like this in Facebook. In our prior work, we conducted a study to address this challenge using a simulated interface and think-aloud protocol. In this study, we aim to make further progress in understanding the impact of different factors on users' clicking decision in social media through a vignette study that encourages participants to think about realistic scenarios that they might face.
Long-term stable continuous variable entanglement generation in type-II non-degenerate optical parametric amplifier

Long-term stable entanglement is a crucial aspect in the implementation of reliable quantum information processes. However, long-term continuous variable entanglement generation, especially in type-II non-degenerate optical parametric amplifier, has yet to be reported on. Here, we derive the relationship between entanglement and temperature fluctuations in the crystal of a type-II non-degenerate optical parametric amplifier, and propose a novel method for long-term stable entanglement generation by locking the temperature of the crystal. In the experiment, we obtain a 5.4 dB entanglement lasting two hours. The method holds promise in the generation of a truly usable above 10 dB entanglement and brings us closer to continuous-variable quantum information processing.
Minority oversampling based on the attraction‐repulsion Weber problem

Summary Learning on imbalanced datasets, where one class is underrepresented, is problematic and important at the same time. On the one hand, a limited number of positive examples restricts the generalization ability of classifiers. On the other hand, often, the class of interest is such exactly because it is rare. The Synthetic Minority Oversampling TEchnique (SMOTE) is a preprocessing method that creates new synthetic examples by interpolating between neighboring instances. In this work, an enhancement to SMOTE is proposed, which characterizes synthetic instances as solutions of attraction‐repulsion problems among the neighboring data points. Experimental evaluation shows an improvement in the positive predictive power of classification.
Desmogging of Smog Affected Images Using Illumination Channel Prior

Visibility restoration of smoggy images plays a significant role in various computer vision applications. However, designing an efficient desmogging technique is still a challenging issue. The majority of existing researchers have designed restoration model for rainy, dusty, foggy, hazy, etc., images only. Therefore, these approaches perform poorly for smoggy images. In this paper, a novel illumination channel prior is proposed to restore smoggy images in a significant way. A gradient magnitude-based filter is also utilized to refine the transmission map. The proposed desmogging approach is compared with the existing visibility restoration approaches over ten real-time smoggy images. The subjective and quantitative analysis reveals that the proposed desmogging approach outperforms others.
Influence-aware Memory for Deep Reinforcement Learning

Making the right decisions when some of the state variables are hidden, involves reasoning about all the possible states of the environment. An agent receiving only partial observations needs to infer the true values of these hidden variables based on the history of experiences. Recent deep reinforcement learning methods use recurrent models to keep track of past information. However, these models are sometimes expensive to train and have convergence difficulties, especially when dealing with high dimensional input spaces. Taking inspiration from influence-based abstraction, we show that effective policies can be learned in the presence of uncertainty by only memorizing a small subset of input variables. We also incorporate a mechanism in our network that learns to automatically choose the important pieces of information that need to be remembered. The results indicate that, by forcing the agent's internal memory to focus on the selected regions while treating the rest of the observable variables as Markovian, we can outperform ordinary recurrent architectures in situations where the amount of information that the agent needs to retain represents a small fraction of the entire observation input. The method also reduces training time and obtains better scores than methods that stack multiple observations to remove partial observability in domains where long-term memory is required.
Controlling Text Complexity in Neural Machine Translation

This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence-to-sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently.
Face Recognition in Real-world Internet Videos Based on Deep Learning

Though current face recognition systems perform well in relatively constrained scenes, they are often affected by secondary creation of netizens, serious image blurring and abundant posture changes in real-world Internet videos. Focusing on these problems, we propose a face recognition model names Internet Video-based Face Recognition Network (IVFRNet) based on deep learning for real Internet videos. And we propose a weighted loss function to enhance the ability of learned features. To test the model, we construct a small-scale real-world Internet video-based face dataset. The experiment results show that our method outperforms the origin method.
Posing Fair Generalization Tasks for Natural Language Inference

Deep learning models for semantics are generally evaluated using naturalistic corpora. Adversarial methods, in which models are evaluated on new examples with known semantic properties, have begun to reveal that good performance at these naturalistic tasks can hide serious shortcomings. However, we should insist that these evaluations be fair -that the models are given data sufficient to support the requisite kinds of generalization. In this paper, we define and motivate a formal notion of fairness in this sense. We then apply these ideas to natural language inference by constructing very challenging but provably fair artificial datasets and showing that standard neural models fail to generalize in the required ways; only task-specific models that jointly compose the premise and hypothesis are able to achieve high performance, and even these models do not solve the task perfectly.
Failure recognition method of rolling bearings based on the characteristic parameters of compressed data and FCM clustering

Compressive sensing retains the substantive characteristics of the original signal based on several sampling values and can realize distortionless reconstruction of signals with high probability. Hence, compressive sensing solves the contradictions among massive sampling data, signal processing spe ed and hardware equipment in the framework of traditional Shannon’s sampling theorem. In this study, as a means of compressive sensing, a new failure recognition method of rolling bearing based on the characteristic parameters of compressed data and fuzzy-C mean (FCM) clustering is proposed. In this method, kurtosis, variance and waveform factor are used as the characteristic parameters. Using the proposed method, the sensitive characteristics were extracted by using the compressed information directly, and the eigenvectors of the sample signals were classified and identified through FCM clustering. In the experiment, the proposed method was compared with different compressed matrix methods and the traditional signal acquisition methods under the same axis diameter and different axis diameters. The results demonstrated the improved performance of the proposed method compared to the other methods.
A clustering approach for mineral potential mapping: A deposit-scale porphyry copper exploration targeting

This work describes a knowledge-guided clustering approach for mineral potential mapping (MPM), by which the optimum number of clusters is derived form a knowledge-driven methodology through a concentration-area (C-A) multifractal analysis. To implement the proposed approach, a case study at the North Narbaghi region in the Saveh, Markazi province of Iran, was investigated to discover porphyry Cu-bearing favorability zones. Whereby, various exploratory indicators were extracted from a multidisciplinary geospatial data set comprising of geology, geophysics and geochemistry criteria. Those indicators were prepared from magnetometry and geo-electrical survey, lithogeochemical samples and geological field operation. The optimum number of clusters was obtained by running the knowledge-based methods of index overlay and fuzzy gamma operators, indicating five clusters from the C-A multifractal curve. Accessing to exploratory drilling lets us to find out the most efficient synthesized favorability map that was generated by a fuzzy algebraic sum operator (or a gamma value equal to one). Assuming the optimum number of clusters, three clustering methods, namely fuzzy C-means (FCM), K-means and self-organizing map were examined for MPM. Note that the FCM as an unsupervised data-driven methodology, had superiority over other clustering analyses by generating mineral favorability map in close association with drilling results.
Porous Lattice-based Transformer Encoder for Chinese NER

Incorporating lattices into character-level Chinese named entity recognition is an effective method to exploit explicit word information. Recent works extend recurrent and convolutional neural networks to model lattice inputs. However, due to the DAG structure or the variable-sized potential word set for lattice inputs, these models prevent the convenient use of batched computation, resulting in serious inefficient. In this paper, we propose a porous lattice-based transformer encoder for Chinese named entity recognition, which is capable to better exploit the GPU parallelism and batch the computation owing to the mask mechanism in transformer. We first investigate the lattice-aware self-attention coupled with relative position representations to explore effective word information in the lattice structure. Besides, to strengthen the local dependencies among neighboring tokens, we propose a novel porous structure during self-attentional computation processing, in which every two non-neighboring tokens are connected through a shared pivot node. Experimental results on four datasets show that our model performs up to 9.47 times faster than state-of-the-art models, while is roughly on a par with its performance. The source code of this paper can be obtained from https://github.com/xxx/xxx.
Mempertahankan keaslian arsip elektronik di era digital berdasarkan tinjauan literatur sistematis

Introduction. This research aimed to understand efforts should be taken so that the authenticity of electronic records can be maintained in the digital environment. Data Collection Method. This research used a systematic literature review published by scientific journals from January 2010 to April 2019 derived from electronic information sources subscribed by the University of Indonesia Library. The results of this review are reported using guidelines for Systematic Literature Review (SLR) which has three stages, namely planning, implementation, and reporting.Analysis Data. Descriptive quantitative was employed to analyze the data by using simple statistic application (Microsoft Office Excel 2007). Results and Discussion. The results indicated that there are 18 literatures (3%) of the total search results of 561 relevant literatures discussing electronic records and ways to maintain their authenticity in digital era. There were 12 articles providing sufficient information about this matter.Conclusion. The majority of the articles mentioned that to maintain the authenticity of electronic archives in digital era must be supported by policy makers such as the government. In addition, the knowledge and skills of archival managers, as well as appropriate technology are also needed.
Path Planning of Unmanned Aerial Vehicles: Current State and Future Challenges

Path planning is the preliminary requirement of unmanned aerial vehicles (UAVs) for their autonomous functions. This paper discusses the significant usage of UAVs in distinct applications and the need for path planning in order to increase their service rate in different applications. UAV’s path planning can be either start to goal or coverage path planning. Path planning techniques can be generally categorized as roadmap/skeleton, approximate/exact cell decomposition, potential field, sampling-based, and bio-inspired/machine learning-based methods. These methods are briefly discussed in this paper. Finally, the present state of the art of UAV’s path planning using these techniques is discussed. This paper will be a seed source for the researchers who are actively working on UAVs to implement efficient path planning techniques according to their usability in different applications.
Capella: Customizing Perception for Edge Devices by Efficiently Allocating FPGAs to DNNs

Deep neural networks (DNNs) have seen resurgent attraction to be implemented in edge applications. However, such implementations are not easy to achieve because execution of DNNs often require more resources than those provided by individual edge devices. On the other hand, relying on model-level distribution methods to implement a DNN on connected edge devices leads to costly communication overheads. To utilize available in-the-edge resources with less communication overhead, we propose using edge-tailored models comprised of nearly-independent narrow DNNs, the inference of which are accelerated using small cost-efficient RISC-based engines. We implement these engines on PYNQ boards as a platform that mimics the limited resources of edge devices. We create the narrow DNNs based on the available resources of PYNQ boards, and allocate each narrow DNN to one engine, implemented in an FPGA. We compare the communication overhead of our implantation against the state-of-the-art model-level distribution methods.
A simplified machine learning approach for recognizing human activity

With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition. Irrespective of the presence of various such devices, they are not adequate to meet dynamic monitoring demands of the visual surveillance system, and their features are highly limited towards complex human activity recognition system. Review of existing system confirms that still there is a large scope of enhancement as they lack applicability to real-life events and also doesn't offer optimal system performance. Therefore, the proposed manuscript presents a model for activity recognition system where the accuracy of recognition operation and system performance are retained with good balance. The study presents a simplified feature extraction process from spatial and temporal traits of the event feeds that is further subjected to the machine learning mechanism for boosting recognition performance
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent

Stochastic compositional optimization arises in many important machine learning tasks such as reinforcement learning and portfolio management. The objective function is the composition of two expectations of stochastic functions, and is more challenging to optimize than vanilla stochastic optimization problems. In this paper, we investigate the stochastic compositional optimization in the general smooth non-convex setting. We employ a recently developed idea of \textit{Stochastic Recursive Gradient Descent} to design a novel algorithm named SARAH-Compositional, and prove a sharp Incremental First-order Oracle (IFO) complexity upper bound for stochastic compositional optimization: $\mathcal{O}((n+m)^{1/2} \varepsilon^{-2})$ in the finite-sum case and $\mathcal{O}(\varepsilon^{-3})$ in the online case. Such a complexity is known to be the best one among IFO complexity results for non-convex stochastic compositional optimization. Numerical experiments validate the superior performance of our algorithm and theory.
Automatic Analysis of Moving Particles by Total Internal Reflection Fluorescence Microscopy

Using of TIRF microscopy videosequences allows to study the propagation of intracellular protein vesicles to the cell surface upon insulin stimulation with high spatio-temporal resolution. Traditional tracking algorithms make mistakes for overlapping points at the intersection trajectories. Therefore, a particle registration algorithm based on background correction has shown optimal results for detecting moving particles or particles of variable size. In this paper we test traditional tracking methods and check performance for tracking protein particle in cell from video sequence TIRFM microscopy. We test four algorithms for detection vesicles location: Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), Determinant of Hessian and algorithm on base continues brightness analysis. In our opinion for determination of position of protein vesicles the best algorithm is based on continues brightness analysis. There are many algorithms for tracking single objects and multitracking algorithms based on them. We used a function that takes a tracker type as input and creates a tracker object. We analyzed seven different multitracking algorithms with realization in OpenCV (BOOSTING, MIL, KCF, TLD, MEDIANFLOW, MOSSE, CSRT) and additional tracker on base of optical flow. In such tests, MEDIANFLOW tracker gives best results for predictable motion and small displacements. This tracker gives an error message in case of incorrect tracking, unlike other trackers that continue to work even the tracking failed.
Design of Cyber-Human Frameworks for Immersive Learning

This paper focuses on the creation of information centric Cyber-Human Learning Frameworks involving Virtual Reality based mediums. A generalized framework is proposed, which is adapted for two educational domains: one to support education and training of residents in orthopedic surgery and the other focusing on science learning for children with autism. Users, experts and technology based mediums play a key role in the design of such a Cyber-Human framework. Virtual Reality based immersive and haptic mediums were two of the technologies explored in the implementation of the framework for these learning domains. The proposed framework emphasizes the importance of Information-Centric Systems Engineering (ICSE) principles which emphasizes a user centric approach along with formalizing understanding of target subjects or processes for which the learning environments are being created.
Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations

212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.