Reviewerly/exHarmony huggingface dataset:
An Analysis of Image Forgery Detection Techniques

Society is becoming increasingly dependent on the internet and so does it become more and more vulnerable to harmful threats. These threats are becoming vigorous and continuously evolving. These threats distorted the authenticity of data transmitted through the internet. As we all completely or partially rely upon this transmitted data. Hence, its authenticity needs to be preserved. Images have the potential of conveying much more information as compared to the textual content. We pretty much believe everything that we see. In order to preserve/check the authenticity of images, image forgery detection techniques are expanding its domain. Detection of forgeries in digital images is in great need in order to recover the people’s trust in visual media. This paper is going to discuss different types of image forgery and blind methods for image forgery detection. It provides the comparative tables of various types of techniques to detect image forgery. It also gives an overview of different datasets used in various approaches of forgery detection.
Extending a sentiment lexicon with synonym–antonym datasets: SWNetTR++

In our previous studies on developing a general-purpose Turkish sentiment lexicon, we constructed SWNetTR-PLUS, a sentiment lexicon of 37K words. In this paper, we show how to use Turkish synonym and antonym word pairs to extend SWNetTR-PLUS by almost 33 % to obtain SWNetTR++, a Turkish sentiment lexicon of 49K words. The extension was done by transferring the problem into the graph domain, where nodes are words, and edges are synonym--antonym relations between words, and propagating the existing tone and polarity scores to the newly added words using an algorithm we have developed. We tested the existing and new lexicons using a manually labeled Turkish news media corpus of 500 news texts. The results show that our method yielded a significantly more accurate lexicon than SWNetTR-PLUS, resulting in an accuracy increase from 72.2 % to 80.4 %. At this level, we have now maximized the accuracy rates of translation-based sentiment analysis approaches, which first translate a Turkish text to English and then do the analysis using English sentiment lexicons.
Rapid Flipping of Parametric Phase States

We experimentally demonstrate flipping the phase state of a parametron within a single period of its oscillation. A parametron is a binary logic element based on a driven nonlinear resonator. It features two stable phase states that define an artificial spin. The most basic operation performed on a parametron is a bit flip between these two states. Thus far, this operation involved changing the energetic population of the resonator and therefore required a number of oscillations on the order of the quality factor $Q$. Our technique takes a radically different approach and relies on rapid control of the underlying potential. Our work represents a paradigm shift for phase-encoded logic operations by boosting the speed of a parametron bit flip to its ultimate limit.
Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints

Pattern mining is a powerful tool for analysing big datasets. Temporal datasets include time as an additional parameter. This leads to complexity in algorithmic formulation, and it can be challenging to process such data quickly and efficiently. In addition, errors or uncertainty can exist in the timestamps of data, for example in manually recorded health data. Sometimes we wish to find patterns only within a certain temporal range. In some cases real-time processing and decision-making may be desirable. All these issues increase algorithmic complexity, processing times and storage requirements. In addition, it may not be possible to store or process confidential data on public clusters or the cloud that can be accessed by many people. Hence it is desirable to optimise algorithms for standalone systems. In this paper we present an integrated approach which can be used to write efficient codes for pattern mining problems. The approach includes: (1) cleaning datasets with removal of infrequent events, (2) presenting a new scheme for time-series data storage, (3) exploiting the presence of prior information about a dataset when available, (4) utilising vectorisation and multicore parallelisation. We present two new algorithms, FARPAM (FAst Robust PAttern Mining) and FARPAMp (FARPAM with prior information about prior uncertainty, allowing faster searching). The algorithms are applicable to a wide range of temporal datasets. They implement a new formulation of the pattern searching function which reproduces and extends existing algorithms (such as SPAM and RobustSPAM), and allows for significantly faster calculation. The algorithms also include an option of temporal restrictions in patterns, which is available neither in SPAM nor in RobustSPAM. The searching algorithm is designed to be flexible for further possible extensions. The algorithms are coded in C++, and are highly optimised and parallelised for a modern standalone multicore workstation, thus avoiding security issues connected with transfers of confidential data onto clusters. FARPAM has been successfully tested on a publicly available weather dataset and on a confidential adult social care dataset, reproducing results obtained by previous algorithms in both cases. It has been profiled against the widely used SPAM algorithm (for sequential pattern mining) and RobustSPAM (developed for datasets with errors in time points). The algorithm outperforms SPAM by up to 20 times and RobustSPAM by up to 6000 times. In both cases the new algorithm has better scalability.
Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model

We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing. 1 Our systems are based on sequence-tosequence modeling.To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py.We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model.We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2.0).Our best system achieves a score of 84.8% F 1 in the DRS parsing shared task.
Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform ( https://www.evidencio.com ) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation.Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation.Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods.This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.
M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Abstract The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm (M-HMOGA) is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and Regional Weighted Run Length features. The experimentations are carried out on Bangla , Devanagari , and Roman numeral datasets, which are the three most popular scripts used in the Indian subcontinent. In-house Bangla and Devanagari script datasets and Competition on Handwritten Digit Recognition (HDRC) 2013 Roman numeral dataset are used for evaluating our model. Moreover, as proof of robustness, we have applied an innovative approach of using different datasets for training and testing. We have used in-house Bangla and Devanagari script datasets for training the model, and the trained model is then tested on Indian Statistical Institute numeral datasets. For Roman numerals, we have used the HDRC 2013 dataset for training and the Modified National Institute of Standards and Technology dataset for testing. Comparison of the results obtained by the proposed model with existing HMOGA and MOGA techniques clearly indicates the superiority of M-HMOGA over both of its ancestors. Moreover, use of K-nearest neighbor as well as multi-layer perceptron as classifiers speaks for the classifier-independent nature of M-HMOGA. The proposed M-HMOGA model uses only about 45–50% of the total feature set in order to achieve around 1% increase when the same datasets are partitioned for training-testing and a 2–3% increase in the classification ability while using only 35–45% features when different datasets are used for training-testing with respect to the situation when all the features are used for classification.
2019 Joint Agency Commercial Imagery Evaluation—Land remote sensing satellite compendium

First posted June 17, 2019 For additional information, contact: Director, Earth Resources Observation and Science Center (EROS)U.S. Geological Survey47914 252nd Street Sioux Falls, SD 57198 The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the first edition of the JACIE compendium, which is planned to be updated and released annually.
Neural Network Alignment for Sentential Paraphrases

We present a monolingual alignment system for long, sentence-or clause-level alignments, and demonstrate that systems designed for word-or short phrase-based alignment are illsuited for these longer alignments.Our system is capable of aligning semantically similar spans of arbitrary length.We achieve significantly higher recall on aligning phrases of four or more words and outperform state-ofthe-art aligners on the long alignments in the MSR RTE corpus.
Like a Baby: Visually Situated Neural Language Acquisition

We examine the benefits of visual context in training neural language models to perform next-word prediction.A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test.Fine-tuning the embeddings of a pre-trained state-of-theart bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement.The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, ∆-RNN, as well as those that use BERT embeddings).Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment.This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.
Content-aware video encoding for cloud gaming

Cloud gaming allows users with thin-clients to play complex games on their end devices as the bulk of processing is offloaded to remote servers. A thin-client is only required to have basic decoding capabilities which exist on most modern devices. The result of the remote processing is an encoded video that gets streamed to the client. As modern games are complex in terms of graphics and motion, the encoded video requires high bandwidth to provide acceptable Quality of Experience (QoE) to end users. The cost incurred by the cloud gaming service provider to stream the encoded video at such high bandwidth grows rapidly with the increase in the number of users. In this paper, we present a content-aware video encoding method for cloud gaming (referred to as CAVE) to improve the perceptual quality of the streamed video frames with comparable bandwidth requirements. This is a challenging task because of the stringent requirements on latency in cloud gaming, which impose additional restrictions on frame sizes as well as processing time to limit the total latency perceived by clients. Unlike many of the previous works, the proposed method is suitable for the state-of-the-art High Efficiency Video Coding (HEVC) encoder, which by itself offers substantial bitrate savings compared to prior encoders. The proposed method leverages information from the game such as the Regions-of-Interest (ROIs), and optimizes the quality by allocating different amounts of bits to various areas in the video frames. Through actual implementation in an open-source cloud gaming platform, we show that the proposed method achieves quality gains in ROIs that can be translated to bitrate savings between 21% and 46% against the baseline HEVC encoder and between 12% and 89% against the closest work in the literature.
$$4\times 4$$ unextendible product basis and genuinely entangled space

We show that there are six inequivalent $$4\times 4$$ unextendible product bases (UPBs) of size eight, when we consider only 4-qubit product vectors. We apply our results to construct positive-partial-transpose entangled states of rank nine. They are at the same time 4-qubit, $$2\times 2\times 4$$ and $$4\times 4$$ states, and their ranges have product vectors. One of the six UPBs turns out to be orthogonal to an incompletely genuinely entangled space, in the sense that the latter does not contain $$4\times 4$$ product vector in any bipartition of 4-qubit systems. We also show that the multipartite UPB orthogonal to a genuinely entangled space exists if and only if the $$n\times n\times n$$ UPB orthogonal to a genuinely entangled space exists for some n. These results help understand an open problem in Demianowicz and Augusiak (Phys Rev A 98:012313, 2018).
Lexicon based Chinese language sentiment analysis method

The growing number of social media users and vast volume of posts could provide valuable information about the sentiment toward different locations, services as well as people. Recent advances in Big Data analytics and natural language processing often means to automatically calculate sentiment in these posts. Sentiment analysis is challenging and computationally demanding task due to the volume of data, misspelling, emoticons as well as abbreviations. While significant work was directed toward the sentiment analysis of English text there is limited attention in literature toward the sentiment analytic of Chinese language. In this work we propose method to identify the sentiment in Chinese social media posts and to test our method we rely on posts sent by visitors of Great Barrier Reef by users of most popular Chinese social media platform Sina Weibo. We elaborate process of capturing of weibo posts, describe a creation of lexicon as well as develop and explain algorithm for sentiment calculation. In case study, related to sentiment toward the different GBR destinations, we demonstrate that the proposed method is effective in obtaining the information and is suitable to monitor visitors? opinion.
Smartphone text input: Effects of experience and phrase complexity on user performance, physiological reaction, and perceived usability

Smartphones have become a common tool of daily communication, and a number of text input methods are extensively used. Qwerty and T9 are two universally used smartphone input methods that differ in user experience (UX) when entering phrases with different complexity. Phrase complexity is an indication of the number and type of characters used in a phrase. This study evaluated the impact of both experience and phrase complexity on the UX of Qwerty and T9. Thirty-two participants with different experience levels (high and low) used both Qwerty or T9 to input 40 phrases (both simple and complex phrases). Input time, speed, error rate, galvanic skin response, heart rate, and perceived usability were analyzed as dependent variables. The results indicated that Qwerty was more effective than T9 for entering complex phrases, while no obvious difference was found for simple phrases. Experience had a positive impact on the input method, and Qwerty was more helpful for participants with low experience level. Performance, physiological reaction, and perceived usability data showed similar results. These findings aid the optimal design of text input systems for smartphones.
Event-driven stitching for tile-based live 360 video streaming

360 video streaming is gaining popularity because of the new type of experience it creates. Tile-based approaches have been widely used in VoD 360 video streaming to save the network bandwidth. However, they cannot be extended to the case of live streaming because they assume the 360 videos stitched offline before streaming. Instead, stitching has to be done in real-time in live 360 video streaming. More importantly, the stitching speed as shown in our experiments is one order of magnitude lower than the network transmission speed, making stitching more of a deciding factor of the overall frame rate than the network transmission speed. In this paper, we design a stitching algorithm for tile-based live 360 video streaming that adapts stitching quality to make the best use of the timing budget. There are two main challenges. First, existing tile-based approaches do not consider various semantic information in different scenarios. Second, the decision of tiling schemes for tile-based stitching is non-trivial. To solve the above two challenges, we present an event-driven stitching algorithm for tile-based 360 video live streaming, which consists of such an event-driven model to abstract various semantic information as events and a tile actuator to make tiling scheme decisions. We implement a streaming system based on event-driven stitching called LiveTexture. To evaluate the proposed algorithm, we compare LiveTexture with other baseline systems and show that LiveTexture adapts well to various timing budgets by meeting 89.4% of the timing constraints. We also demonstrate that LiveTexture utilizes the timing budget more efficiently than others.
Searching Software Knowledge Graph with Question

Researchers have constructed a variety of knowledge repositories/bases in different domains. These knowledge repositories generally use graph database (Neo4j) to manage heterogeneous and widely related domain data, which providing structured query (i.e., Cypher) interfaces. However, it is time-consuming and labor-intensive to construct a structured query especially when the query is very complex or the scale of the knowledge graph is large. This paper presents a natural language question interface for software knowledge graph. It extracts meta-model of software knowledge repository, constructs question related Inference Sub-Graph, then automatically transfers natural language question to structured Cypher query and returns the corresponding answer. We carry out our experiments on two famous open source software projects, build their knowledge graphs and verify our approach can accurately answer almost all the questions on the corresponding knowledge graph.
ClaimPortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on Twitter

We present ClaimPortal, a web-based platform for monitoring, searching, checking, and analyzing English factual claims on Twitter.We explain the architecture of ClaimPortal, its components and functions, and the user interface.While the last several years have witnessed a substantial growth in interests and efforts in the area of computational factchecking, ClaimPortal is a novel infrastructure in that fact-checkers have largely skipped factual claims in tweets.It can be a highly powerful tool to both general web users and factcheckers.It will also be an educational resource in helping cultivate a society that is less susceptible to falsehoods.While it currently focuses on politics-related tweets, it will be extended to include more general factual claims.
A Fast kNN-Based Approach for Time Sensitive Anomaly Detection over Data Streams

Anomaly detection is an important data mining method aiming to discover outliers that show significant diversion from their expected behavior. A widely used criteria for determining outliers is based on the number of their neighboring elements, which are referred to as Nearest Neighbors (NN). Existing kNN-based Anomaly Detection (kNN-AD) algorithms cannot detect streaming outliers, which present time sensitive abnormal behavior characteristics in different time intervals. In this paper, we propose a fast kNN-based approach for Time Sensitive Anomaly Detection (kNN-TSAD), which can find outliers that present different behavior characteristics, including normal and abnormal characteristics, within different time intervals. The core idea of our proposal is that we combine the model of sliding window with Locality Sensitive Hashing (LSH) to monitor streaming elements distribution as well as the number of their Nearest Neighbors as time progresses. We use an $$\epsilon $$ -approximation scheme to implement the model of sliding window to compute Nearest Neighbors on the fly. We conduct widely experiments to examine our approach for time sensitive anomaly detection using three real-world data sets. The results show that our approach can achieve significant improvement on recall and precision for anomaly detection within different time intervals. Especially, our approach achieves two orders of magnitude improvement on time consumption for streaming anomaly detection, when compared with traditional kNN-based anomaly detection algorithms, such as exact-Storm, approx-Storm, MCOD etc, while it only uses 10% of memory consumption.