Reviewerly/exHarmony huggingface dataset:
Discriminatively Relabel for Partial Multi-label Learning

Partial multi-label learning (PML) deals with the problem where each training example is assigned multiple candidate labels, only a part of which are correct. To learn from such PML examples, the straightforward model training tends to be misled by the noise candidate label set. To alleviate this problem, a coupled framework is established in this paper to learn the desired model and perform the relabeling procedure alternatively. In the relabeling procedure, instead of simply extracting relative label confidences, or deterministically eliminating low confidence labels and preserving high confidence labels as ground-truth ones, we introduce a soft sign thresholding operator to adaptively strengthen candidate labels with high confidence and weaken candidate labels with low confidence, which enlarges the difference of confidences of candidate labels within allowable range. We further show that the resulting nonconvex quadratic programming (QP) optimization problem can be relaxed into a convex QP problem with proper conditions. Extensive experiments on synthesized and real-world data sets demonstrate the effectiveness of our proposed approach.
PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words

Sentiment analysis is probably the most actively growing area of natural language processing nowadays, which leverages huge amount of user-contributed data on Internet to improve income of businesses and quality of life of consumer. The majority of existent sentiment-analysis systems is focused on English, due to lack of resources and tools for other languages. To fill this gap for Persian language, in our previous work we have compiled the first version of PerSent Persian sentiment lexicon, which was small and included only words and phrases from general domain. In this paper, we present its extension with words from three different domains and evaluate its performance on polarity classification task using various machine learning-based classifiers. We use a multi-domain dataset to evaluate the performance of our new lexicon on various domains. Our results demonstrate usefulness of the new lexicon for analysis of product and movie reviews and especially of political news in Persian language.
A Novel Approach for Outlier Detection and Robust Sensory Data Model Learning

In the past few decades machine learning and data analysis have been having a huge growth and they have been applied in many different problems in the field of robotics. Data are usually the result of sensor measurements and, as such, they might be subjected to noise and outliers. The presence of outliers has a huge impact on modelling the acquired data, resulting in inappropriate models. In this work a novel approach for outlier detection and rejection for input/output mapping in regression problems is presented. The robustness of the method is shown both through simulated data for linear and nonlinear regression, and real sensory data. Despite being validated by using artificial neural networks, the method can be generalized to any other regression method.
Exploring the Mediating Effect of Demographic Attributes on the Relationship between Employee Engagement and Organizational Citizenship Behavior

Organizations have long been concerned on how management can facilitate Organizational Citizenship Behavior (OCB) and employee engagement (EE) which can result in higher performance and productivity. This study attempted to test the mediating role of demographic attributes on the relationship between employee engagement and organizational citizenship behavior in the context of local enterprises in Dhaka city, Bangladesh. The authors administered 524 participants along with on employee engagement scale (5 factors) and organizational citizenship scale (5 factors). After the validation of the model only 3 factors from OCB and 4 factors from EE have been retained. The study finally reveals the Employee Engagement (EE) has a positively significant impact on OCB. However, in mediating effect the demographic attributes (Experience) partially mediates the relationship on OCB factors but do not mediate on EE. Rest of the mediating effect of gender and age were not supported the hypothesis.
A Parallel and Forward Private Searchable Public-Key Encryption for Cloud-Based Data Sharing

Data sharing through the cloud is flourishing with the development of cloud computing technology. The new wave of technology will also give rise to new security challenges, particularly the data confidentiality in cloud-based sharing applications. Searchable encryption is considered as one of the most promising solutions for balancing data confidentiality and usability. However, most existing searchable encryption schemes cannot simultaneously satisfy requirements for both high search efficiency and strong security due to lack of some must-have properties, such as parallel search and forward security. To address this problem, we propose a variant searchable encryption with parallelism and forward privacy, namely the parallel and forward private searchable public-key encryption (PFP-SPE). PFP-SPE scheme achieves both the parallelism and forward privacy at the expense of slightly higher storage costs. PFP-SPE has similar search efficiency with that of some searchable symmetric encryption schemes but no key distribution problem. The security analysis and the performance evaluation on a real-world dataset demonstrate that the proposed scheme is suitable for practical application.
Asymmetric image encryption based on optical interference using modified error-reduction phase retrieval algorithm

In this paper, we propose an asymmetric interference based encryption (IBE) using a modified error-reduction phase retrieval algorithm (mERPRA) in a structure of optical interference. Unlike previously proposed IBE schemes, the ciphertext in our scheme has less pixels than the original image. What is more important, a plaintext-dependent private key can be retrieved by using mERPRA with a fixed amplitude constraint, which makes this IBE scheme asymmetric. As we know, except for equal modulus decomposition-based encryption schemes, this is the first time that asymmetric IBE is presented. Consequentially, our proposed IBE scheme gains enhanced security due to its asymmetry. Simulation results are demonstrated to prove the validity of this scheme.
Virtual reality: educational and methodological aspects

Problem and goal. Virtual reality in education is used as the information space, in which the student can not only obtain the necessary information, but also enter into the contact with the fictitious objects. Sinking (immersion) into the educational medium helps students to acquire the habits of interaction with the virtual objects, to develop methods of collective collaboration and to find confidence in the course presentation of its own projects. The process of creating the resources with the elements of interactivity, and also the selection of the necessary instrument means is a technological and a systematic problem for the teachers, who are mastering new information technologies. The purpose of the article is construction and substantiation of the effectiveness of the model of the use of technologies of virtual reality in the course of the instruction of schoolchildren in the system of additional formation. Methodology. The development of the model of the use of technologies of virtual reality in the instruction of students in the extra-curriculum activity was accomplished on the basis of the complex data analysis, obtained in the course of conducting the approval developed educational methods materials in the system of additional formation, structuring of information, generalization of the results of a study. Results. The didactic possibilities of the technologies of virtual reality were identified in the course of the instruction of students in the system of additional formation. The theoretical substantiation is given the need of applying the virtual reality as a means of the development of creative thinking in students and an effective tool of the creation of the immersive training medium. On the basis of a study working program has been prepared, as well as teaching aid and systematic recommendations for the teachers, who use technologies of virtual reality for conducting the training exercises. Conclusion. The results of the conducted investigation made it possible to make a conclusion about the effectiveness of the use of technologies of virtual reality in the system of additional formation. It is ensured by the goal-directed application of the corresponding model of instruction and system of instrumental and teaching aids. The application of virtual reality, different technologies of imitation simulation not only contributes to the development of the algorithmic and creative thinking of students, but also to the maximum disclosure of their intellectual potential.
SPGD and Newton iteration mixed algorithm used in freeform surface metrology

Freeform surfaces are widely used in advanced optical systems. In order to accurately measure freeform surfaces, an adaptive interferometer with deformable mirror (DM) has been developed for the freeform surface metrology. To ensure the efficiency, a stochastic parallel gradient descent (SPGD) and Newton iteration mixed algorithm to drive the DM has been proposed, it can achieve the minimum value of the optimization and is suitable for the different interferograms. The simulation and experimental results verify the correctness and feasibility of the proposed DM driving algorithm.
Frequency Separation Network for Image Super-Resolution

It is well-known that high-frequency information (e.g.textures, edges) is significant for single image super-resolution (SISR).However, Existing of deep Convolutional Neural Network (CNN) based methods directly model mapping function from low resolution (LR) to high resolution (HR), and they treat high-frequency and low-frequency information equally during feature extraction.Therefore, the highfrequency learning mode can not be sufficiently attentive, resulting in inaccurate representation of some local details.In this study, we aim to build potential frequencies' relations and handle high-frequency and low-frequency information differentially.Specifically, we propose a novel Frequency Separation Network (FSN) for image super-resolution (SR).In FSN, a new Octave Convolution (OC) is adopted, which uses four operations to perform information update and frequency communication between high frequency and low frequency features.In addition, global and hierarchical feature fusion are employeed to learn elaborate and comprehensive feature representations, in order to further benefit the quality of final image reconstruction.Extensive experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our method.
Neural architectures for gender detection and speaker identification

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
HCI on the Table: Robust Gesture Recognition Using Acoustic Sensing in Your Hand

The paper proposes a new HCI mechanism for device-free gesture recognition on the table using acoustic signal, which can extend the gesture input and interactions beyond the tiny screen of mobile device and allow users to provide input without blocking screen view. Previous researches have either relied on additional devices (e.g., special wearable device and mouse) or required active acoustic signals which demand additional cost and be less prone to popularize, while we explore the device-free gesture recognition using passive acoustic signals. This technology is more challenging due to the lack of an effective approach to eliminate the inherent ambient noise disturbances and extract stable gesture features. We fuse both short time energy (STE) and zero-crossing rate (ZCR) to identify the effective signals from the original input, and leverage the Mel frequency cepstral coefficients (MFCC), cochlear filter cepstral coefficients (CFCC) to extract the stable features from different gestures. The unique features in support vector machine (SVM) classifier achieve a high gesture recognition accuracy from the noisy scenarios and mismatched conditions. Implementation on the Android system has realized real-time processing of the feature extraction and gesture recognition. Extensive evaluations show our algorithm has a better noise tolerant performance and the system could recognize seven common gestures (click, flip left/right, scroll up/down, zoom in/out) on smart devices with an accuracy of 93.2%.
Analyzing random permutations for cyclic coordinate descent

We consider coordinate descent methods for minimization of convex quadratic functions, in which exact line searches are performed at each iteration. (This algorithm is identical to Gauss-Seidel on the equivalent symmetric positive definite linear system.) We describe a class of convex quadratic functions for which the random permutations version of cyclic coordinate descent (RPCD) is observed to outperform the standard cyclic coordinate descent (CCD) approach on computational tests, yielding convergence behavior similar to the fully random variant (RCD). A convergence analysis is developed to explain the empirical observations.
Global and local sensitivity guided key salient object re-augmentation for video saliency detection

Image saliency is determined by spatial semantic features, while video saliency is affected by multiple factors such as spatial and temporal information. Since human eyes stay extremely short on each frame, the dynamic salient area is more focused and concentrated on one salient object. In order to better simulate the human visual attention mechanism in dynamic scenes, we propose a key salient object re-augmentation method (KSORA) based on the guidance of both bottom-up weighted features and top-down semantic knowledge. The bottom-up feature weighting strategy effectively eliminates noisy and redundancy, and provide accurate local spatiotemporal features for saliency inference. The top-down key object enhancement strategy ranks salient candidates based on global statistical knowledge, so as to explicitly enhance the saliency proportion of the key object. The fusion of the local weighted spatiotemporal features and the global key object augmentation features not only ensures spatiotemporal consistency, but also facilitates obtaining more concentrated salient prediction. Results on three large datasets validate that our proposed method has the capability of improving the detection accuracy in complex scenes.
MPS-CAE SIMULATION ON DYNAMIC INTERACTION BETWEEN STEEL CASING AND EXISTING PILE WHEN PULLING OUT EXISTING PILES

Many urban areas in Japan are located on soft ground and there are numerous buildings with pile foundations. Therefore, when dismantling a building, it is necessary to remove the piles (existing piles) remaining in the ground that are designated as industrial waste. One method of removing the existing piles consists of crushing and removing the piles, but there is a problem of vibrations and noise. Therefore, in many existing pile-drawing methods, the outer circumference of the pile is drilled by a steel casing, and the existing pile is pulled out by cutting the boundary between the pile and the surrounding ground. When pulling out an existing pile that exists diagonally, due to the pressure in the soil or an error in the construction, it has been confirmed that the steel casing follows the sloped pile when inserted into the ground. However, the dynamic mechanism of this phenomenon is unknown because the situation in the ground cannot be observed visually. In this study, therefore, an MPS-CAE simulation will be used to clarify visually and quantitatively the dynamic mechanism along an existing pile in which the steel casing is inclined when inserted into the ground, which occurs at actual sites
Monitoring online reviews for reputation fraud campaigns

Online reviews are critical for both purchasers and sellers in the era of E-commerce. Praiseful reviews and/or 5-star ratings can yield remarkable profit gains, on the other hand, a bad-mouth review or a low rating score often incurs sales decrease. Therefore, fake review detection has attracted lots of research interests in recent years. While most existing approaches detect fake reviews in an offline fashion, i.e., finding suspicious reviews from a large volume of historical data, few efforts have been made to detect fake reviews in an online fashion, i.e., detecting suspicious reviews in review data streams. Online detecting fake reviews has many more benefits than offline detection in that the damages of fake reviews can be significantly reduced by removing them as early as possible. In this paper, we propose a novel online monitoring technique for detecting reputation fraud campaigns in product reviews. The technique includes two phases. First, it monitors online reviews to generate the most abnormal review subsequences (MARSs), which can be considered as candidate reputation fraud campaigns. Second, conditional random fields are exploited to label each review in a MARS as fake or genuine. Experiments show that our proposed methods are highly effective and efficient, with many advantages compared with existing online detection approaches.
NoJITsu: Locking Down JavaScript Engines

Data-only attacks against dynamic scripting environments have become common.Web browsers and other modern applications embed scripting engines to support interactive content.The scripting engines optimize performance via just-intime compilation.Since applications are increasingly hardened against code-reuse attacks, adversaries are looking to achieve code execution or elevate privileges by corrupting sensitive data like the intermediate representation of optimizing JIT compilers.This has inspired numerous defenses for just-in-time compilers.Our paper demonstrates that securing JIT compilation is not sufficient.First, we present a proof-of-concept data-only attack against a recent version of Mozilla's SpiderMonkey JIT in which the attacker only corrupts heap objects to successfully issue a system call from within bytecode execution at run time.Previous work assumed that bytecode execution is safe by construction since interpreters only allow a narrow set of benign instructions and bytecode is always checked for validity before execution.We show that this does not prevent malicious code execution in practice.Second, we design a novel defense, dubbed NOJITSU to protect complex, real-world scripting engines from data-only attacks against interpreted code.The key idea behind our defense is to enable fine-grained memory access control for individual memory regions based on their roles throughout the JavaScript lifecycle.For this we combine automated analysis, instrumentation, compartmentalization, and Intel's Memory-Protection Keys to secure SpiderMonkey against existing and newly synthesized attacks.We implement and thoroughly test our implementation using a number of real-world scenarios as well as standard benchmarks.We show that NOJITSU successfully thwarts codereuse as well as data-only attacks against any part of the scripting engine while offering a modest run-time overhead of only 5%.
Fault Injection Characterization on Modern CPUs

Recently, several Fault Attacks (FAs) which target modern Central Processing Units (CPUs) have emerged. These attacks are studied from a practical point of view and, due to the modern CPUs complexity, the underlying fault effect is usually unknown. In this article, we focus on the characterization of a perturbation (the fault model) on modern CPU. For that, we introduce the first approach to characterize the fault model on modern CPU from the Instruction Set Architecture (ISA) level to the micro-architectural level. This fault model helps at determining which micro-architecture elements are disrupted and how. Our fault model aims at finding original attack paths and design efficient countermeasures. To confront our approach to real modern CPUs, we apply our approach on ARM and x86 architectures CPUs, mainly on the BCM2837 and an Intel Core i3.
How the Visitors’ Cognitive Engagement Is Driven (but Not Dictated) by the Visibility and Co-visibility of Art Exhibits

The spatial arrangement of artworks is recognized as one of the key elements of exhibition design. The underlying assumption is that the layout can strengthen the impact of individual exhibits, because the way visitors visually engage with artworks affects how they are cognitively processed. This paper explores the influence of the exhibits' visual properties on the visitors' attention and their memory of artworks. Attention was recorded with the use of mobile eye-tracking and memory was measured by an unanticipated recognition test immediately after the visit. The paper analyses both the total amount of attention spent on interacting with each artwork, as well as the strategy through which attention was allocated: through primarily longer ('diligent') looks, versus primarily shorter ('distracted') glimpses. Results of two experiments demonstrate that the visibility and co-visibility of artworks affected the amount of attention allocated to them, and¬¬ the strategy of attention allocation. While the amount of attention contributed to improving the recognition memory of pictures, the strategy of attention allocation did not. These findings demonstrate the power of the exhibition's visual properties to influence the experience of museum visitors but also highlight the visitors' ability to employ alternative viewing strategies without diminishing the cognitive processing of artworks.
Light-Board: Simple Media to Learn Photosynthesis Concepts

This research is a research and development that aims to develop learning media for LED-based photosynthesis material.This media is designed with a 4D model consisting of four stages, namely define, design, develop, and disseminate.However, this research was only carried out until the development stage due to time constraints.The instrument in this study is the validation sheet consisting of media validation and material validation.The results obtained after validation by media experts with a total of 3 and material experts with a total of 3.1 included in the criteria are quite suitable used as learning media in the classroom.It can be concluded that after the development of LED based learning media are appropriate use for learning in the classroom.
Regularize, Expand and Compress: NonExpansive Continual Learning

Continual learning (CL), the problem of lifelong learning where tasks arrive in sequence, has attracted increasing attention in the computer vision community lately. The goal of CL is to learn new tasks while maintaining the performance on the previously learned tasks. There are two major obstacles for CL of deep neural networks: catastrophic forgetting and limited model capacity. Inspired by the recent breakthroughs in automatically learning good neural network architectures, we develop a nonexpansive AutoML framework for CL termed Regularize, Expand and Compress (REC) to solve the above issues. REC is a unified framework with three highlights: 1) a novel regularized weight consolidation (RWC) algorithm to avoid forgetting, where accessing the data seen in the previously learned tasks is not required; 2) an automatic neural architecture search (AutoML) engine to expand the network to increase model capability; 3) smart compression of the expanded model after a new task is learned to improve the model efficiency. The experimental results on four different image recognition datasets demonstrate the superior performance of the proposed REC over other CL algorithms.