Reviewerly/exHarmony huggingface dataset:
Studying Cultural Differences in Emoji Usage across the East and the West

Global acceptance of Emojis suggests a cross-cultural, normative use of Emojis. Meanwhile, nuances in Emoji use across cultures may also exist due to linguistic differences in expressing emotions and diversity in conceptualizing topics. Indeed, literature in cross-cultural psychology has found both normative and culture-specific ways in which emotions are expressed. In this paper, using social media, we compare the Emoji usage based on frequency, context, and topic associations across countries in the East (China and Japan) and the West (United States, United Kingdom, and Canada). Across the East and the West, our study examines a) similarities and differences on the usage of different categories of Emojis such as People, Food &amp; Drink, Travel &amp; Places etc., b) potential mapping of Emoji use differences with previously identified cultural differences in users’ expression about diverse concepts such as death, money emotions and family, and c) relative correspondence of validated psycho-linguistic categories with Ekman’s emotions. The analysis of Emoji use in the East and the West reveals recognizable normative and culture specific patterns. This research reveals the ways in which Emojis can be used for cross-cultural communication.
Conceptualization in reference production: Probabilistic modeling and experimental testing.

In psycholinguistics, there has been relatively little work investigating conceptualization-how speakers decide which concepts to express. This contrasts with work in natural language generation (NLG), a subfield of artificial intelligence, where much research has explored content determination during the generation of referring expressions. Existing NLG algorithms for conceptualization during reference production do not fully explain previous psycholinguistic results, so we developed new models that we tested in three language production experiments. In our experiments, participants described target objects to another participant. In Experiment 1, either size, color, or both distinguished the target from all distractor objects; in Experiment 2, either color, type, or both color and type distinguished it from all distractors; In Experiment 3, color, size, or the border around the object distinguished the target. We tested how well the different models fit the distribution of description types (e.g., small candle, candle, small gray candle) that participants produced. Across these experiments, the probabilistic referential overspecification model (PRO) provided the best fit. In this model, speakers first choose a property that rules out all distractors. If there is more than one such property, then they probabilistically choose one on the basis of a preference for that property. Next, they sometimes add another property, with the probability again determined by its preference and speakers' eagerness to overspecify. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Neurocomputing fundamental climate analysis

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively.
A Heuristic Approach for Tracking Error and Energy Consumption Minimization in Solar Tracking Systems

This paper proposes a methodology for optimizing a class of robotic solar tracking systems with two degrees of freedom using a heuristic approach. The proposal allows a balance to be found between the energy consumption and tracking accuracy in the tracking system. The main purpose is the behavior modification of the system through the combination of two manipulation strategies, one associated with the energy savings and the other with the tracking error. The heuristic approach was implemented in a solar tracking system with the end effector connected to a solar measurement device. Four energy-saving strategies and three tracking strategies were developed, simulated, and implemented in the system. The simulation results show that the resulting strategy combination (tracking error and energy saving approach) led to 31.55% energy savings compared to the reference values, with a tracking error of 0.06°. Moreover, the experimental assessment of the same combination led to 26.98% energy is being saved, with an azimuthal tracking error of 0.062° and elevation tracking error of 0.071°. The preceding values support the aim of the presented proposal to significantly reduce energy consumption while concurrently achieving a competitive tracking error.
Reshaping the Real Estate Industry Using Blockchain

The advent of blockchain technology has been set to revolutionize the real estate industry, and the potential changes are already taking its shape. The real estate industry is one among the top global sectors that is driving the economic growth of any country. The growth of this industry is well complemented by the growth of the corporate environment, demand for office space, industrial plots, urban housing accommodations, agricultural lands, etc. However, the existing world of real estate is complicated with the lack of transparency in its transactions such as leasing, purchasing, and sales and fails to attain the level of confidentiality and authenticity of operational data. Several aspects of its operations such as property sale prices, sale history, lease rental rates, market valuation, and so on expect greater demand for transparency, data integrity, and security—a trusted environment. As a result, property-related information can be made available as digitized information and hosted as decentralized database of records on distributed systems with lesser incidence of fraud and inaccuracies. The possibilities of mutual delusion and concealment during the sale, rental, and lease processes can be eliminated, thus achieving digitization of real estate ownership. Everything from making real estate investment decisions to choosing investment properties, blockchain can become a common practice by incorporating its wide benefits. This paper aims at revamping the tedious, paper-based, time-consuming system of ownership and renting into a next-generation digitized system based on blockchain technology.
A HEVC Video Steganalysis Algorithm Based on PU Partition Modes

Steganalysis is a technique used for detecting the existence of secret information embedded into cover media such as images and videos. Currently, with the higher speed of the Internet, videos have become a kind of main methods for transferring information. The latest video coding standard High Efficiency Video Coding (HEVC) shows better coding performance compared with the H.264/AVC standard published in the previous time. Therefore, since the HEVC was published, HEVC videos have been widely used as carriers of hidden information. In this paper, a steganalysis algorithm is proposed to detect the latest HEVC video steganography method which is based on the modification of Prediction Units (PU) partition modes. To detect the embedded data, All the PU partition modes are extracted from P pictures, and the probability of each PU partition mode in cover videos and stego videos is adopted as the classification feature. Furthermore, feature optimization is applied, that the 25-dimensional steganalysis feature has been reduced to the 3-dimensional feature. Then the Support Vector Machine (SVM) is used to identify stego videos. It is demonstrated in experimental results that the proposed steganalysis algorithm can effectively detect the stego videos, and much higher classification accuracy has been achieved compared with state-of-the-art work.
How Many Bits Does it Take to Quantize Your Neural Network?

Abstract Quantization converts neural networks into low-bit fixed-point computations which can be carried out by efficient integer-only hardware, and is standard practice for the deployment of neural networks on real-time embedded devices. However, like their real-numbered counterpart, quantized networks are not immune to malicious misclassification caused by adversarial attacks. We investigate how quantization affects a network’s robustness to adversarial attacks, which is a formal verification question. We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. We built a tool and analyzed the effect of quantization on a classifier for the MNIST dataset. We demonstrate that, compared to our method, existing methods for the analysis of real-numbered networks often derive false conclusions about their quantizations, both when determining robustness and when detecting attacks, and that existing methods for quantized networks often miss attacks. Furthermore, we applied our method beyond robustness, showing how the number of bits in quantization enlarges the gender bias of a predictor for students’ grades.
Bias Analysis and Mitigation in the Evaluation of Authorship Verification

The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics. Among others, PAN contributions include original corpora, tailored benchmarks, and standardized experimentation platforms. In this paper we review, theoretically and practically, the authorship verification task and conclude that the underlying experiment design cannot guarantee pushing forward the state of the art—in fact, it allows for top benchmarking with a surprisingly straightforward approach. In this regard, we present a “Basic and Fairly Flawed” (BAFF) authorship verifier that is on a par with the best approaches submitted so far, and that illustrates sources of bias that should be eliminated. We pinpoint these sources in the evaluation chain and present a refined authorship corpus as effective countermeasure.
Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery

Deep Neural Networks (DNNs) have recently received significant attention in the side-channel community due to their state-of-the-art performance in security testing of embedded systems. However, research on the subject mostly focused on techniques to improve the attack efficiency in terms of the number of traces required to extract secret parameters. What has not been investigated in detail is a constructive approach of DNNs as a tool to evaluate and improve the effectiveness of countermeasures against side-channel attacks. In this work, we close this gap by applying attribution methods that aim for interpreting Deep Neural Network (DNN) decisions in order to identify leaking operations in cryptographic implementations. In particular, we investigate three different approaches that have been proposed for feature visualization in image classification tasks and compare them regarding their suitability to reveal Points of Interest (POIs) in side-channel traces. We show by experiments with four separate data sets that the three methods are especially interesting in the context of side-channel protected implementations and misaligned measurements. Finally, we demonstrate that attribution can also serve as a powerful side-channel distinguisher leading to a successful retrieval of the secret key with at least five times fewer traces compared to standard key recovery in DNN-based attack setups.
Color normalization of faded H&amp;E-stained histological images using spectral matching

Histological samples stained with hematoxylin-eosin (H&E) are commonly used by pathologists in cancer diagnoses. However, the preparation, digitization, and storage of tissue samples can lead to color variations that produce poor performance when using histological image processing techniques. Thus, normalization methods have been proposed to adjust the color of the image. This can be achieved through the use of a spectral matching technique, where it is first necessary to estimate the H&E representation and the stain concentration in the image pixels by means of the RGB model. This study presents an estimation method for H&E stain representation for the normalization of faded histological samples. This application has been explored only to a limited extent in the literature, but has the capacity to expand the use of faded samples. To achieve this, the normalized images must have a coherent color representation of the H&E stain with no introduction of noise, which was realized by applying the methodology described in this proposal. The estimation method presented here aims to normalize histological samples with different degrees of fading using a combination of fuzzy theory and the Cuckoo search algorithm, and dictionary learning with an initialization method for optimization. In visual and quantitative comparisons of estimates of H&E stain representation from the literature, our proposed method achieved very good results, with a high feature similarity between the original and normalized images.
Searching nonadiabatic holonomic quantum gates via an optimization algorithm

Nonadiabatic holonomic quantum computation (NHQC) offers a way to realize geometric quantum gates beyond the adiabatic regime. To extend the flexibility and control robustness, many NHQC schemes have been proposed. Here, we propose to use quantum optimal control theory to search control sequences that can realize holonomic gates. Given a target gate and the corresponding computational subspace, our optimization algorithm can find the control sequence automatically. Furthermore, by defining an averaged target function, our method can improve the robustness of the holonomic gates against the errors such as thermal noises and amplitude errors. As an application, we search the control sequences for the Hadamard, not, and cnot gates in a nitrogen-vacancy-center system.
The Natural Language of Actions

We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning. Representing actions in a vector space help reinforcement learning algorithms achieve better performance by grouping similar actions and utilizing relations between different actions. We show how prior knowledge of an environment can be extracted from demonstrations and injected into action vector representations that encode natural compatible behavior. We then use these for augmenting state representations as well as improving function approximation of Q-values. We visualize and test action embeddings in three domains including a drawing task, a high dimensional navigation task, and the large action space domain of StarCraft II.
Recurrent Neural Network for Text Classification with Hierarchical Multiscale Dense Connections

Text classification is a fundamental task in many Natural Language Processing applications. While recurrent neural networks have achieved great success in performing text classification, they fail to capture the hierarchical structure and long-term semantics dependency which are common features of text data. Inspired by the advent of the dense connection pattern in advanced convolutional neural networks, we propose a simple yet effective recurrent architecture, named Hierarchical Mutiscale Densely Connected RNNs (HM-DenseRNNs), which: 1) enables direct access to the hidden states of all preceding recurrent units via dense connections, and 2) organizes multiple densely connected recurrent units into a hierarchical multi-scale structure, where the layers are updated at different scales. HM-DenseRNNs can effectively capture long-term dependencies among words in long text data, and a dense recurrent block is further introduced to reduce the number of parameters and enhance training efficiency. We evaluate the performance of our proposed architecture on three text datasets and the results verify the advantages of HM-DenseRNN over the baseline methods in terms of the classification accuracy.
Improved PRM for Path Planning in Narrow Passages

In view of the shortcomings of Probabilistic Roadmaps (PRM) in the case of narrow passages, an improved method based on optimal sampling strategy is proposed. By sampling the dense area of the obstacles, the sampling points distributed inside the obstacle are selected and uniformly sampled by the distanced, so that the sampling point is generated around the obstacle in the free area, thereby increasing the number of sampling points in the narrow passages. The simulation results show that the improved PRM has more sampling points in the narrow passages than the standard PRM. And the time of path planning, the success rate and the path length are also significantly improved.
Sequential Neural Processes

Neural Processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction in stochastic processes. However, a large class of problems comprise underlying temporal dependency structures in a sequence of stochastic processes that Neural Processes (NP) do not explicitly consider. In this paper, we propose Sequential Neural Processes (SNP) which incorporates a temporal state-transition model of stochastic processes and thus extends its modeling capabilities to dynamic stochastic processes. In applying SNP to dynamic 3D scene modeling, we introduce the Temporal Generative Query Networks. To our knowledge, this is the first 4D model that can deal with the temporal dynamics of 3D scenes. In experiments, we evaluate the proposed methods in dynamic (non-stationary) regression and 4D scene inference and rendering.
Multi-Perspective Discriminators-Based Generative Adversarial Network for Image Super Resolution

Recently, generative adversarial network-based image super resolution has been investigated, and it has been shown to lead to overwhelming improvements in subjective quality. However, it also leads to checkerboard artifacts and the unpleasing high-frequency (HF) components. In this paper, we propose a multi-discriminators-based image super resolution method that distinguishes those artifacts from various perspectives. First, the DCT perspective discriminator is proposed because the checkerboard artifacts are easily separated on the frequency domain. Second, the gradient perspective discriminator is proposed, because the unpleasing HF components can be discriminated on the gradient magnitude distribution. These proposed multi-perspective discriminators can easily identify artifacts, and they can help the generator reproduce artifact-less SR images. The experimental results show that the proposed SR-GAN with multi-perspective discriminators achieves objective and subjective quality improvements in terms of PSNR, SSIM, PI and MOS, as compared to the conventional SR-GAN by reducing the aforementioned artifacts.
Private-Sector Attribution of Cyber Incidents: Benefits and Risks to the U.S. Government

In a blog post in June 2016, the threat intelligence company CrowdStrike attributed the breach of the Democratic National Committee’s (DNC’s) networks to two groups of cyber actors closely linked t...
Data Augmentation for Radio Frequency Fingerprinting via Pseudo-Random Integration

Radio frequency fingerprinting (RFF) is a lightweight access authentication method for the mobile terminal nodes. The classification accuracy of RFF is effectively improved by machine learning, of which the performance is deeply enhanced with the increasing training data. Data augmentation is the most frequently used method to get additional data and superior results with limited data in the classification of images and sounds. This paper proposes a random integration based data augmentation for signal processing via exploring the similarity between signal and deoxyribonucleic acid sequence. An optimized data augmentation based on pseudo-random integration is designed in order to provide additional data, and reduce the instability of classification accuracy caused by randomness simultaneously. Experiments are made to demonstrate performance of each augmentation method using the same model that classifies ten kinds of RF fingerprints with Gaussian support vector machine classifier, and show that pseudo-random integration has an obviously accurate and stable result with the same initial dataset and system, which implies that the performance of RFF could be improved further by applying pseudo-random integration based data augmentation.
Bitcoin and Cybersecurity: Temporal Dissection of Blockchain Data to Unveil Changes in Entity Behavioral Patterns

The Bitcoin network not only is vulnerable to cyber-attacks but currently represents the most frequently used cryptocurrency for concealing illicit activities. Typically, Bitcoin activity is monitored by decreasing anonymity of its entities using machine learning-based techniques, which consider the whole blockchain. This entails two issues: first, it increases the complexity of the analysis requiring higher efforts and, second, it may hide network micro-dynamics important for detecting short-term changes in entity behavioral patterns. The aim of this paper is to address both issues by performing a “temporal dissection” of the Bitcoin blockchain, i.e., dividing it into smaller temporal batches to achieve entity classification. The idea is that a machine learning model trained on a certain time-interval (batch) should achieve good classification performance when tested on another batch if entity behavioral patterns are similar. We apply cascading machine learning principles—a type of ensemble learning applying stacking techniques—introducing a “k-fold cross-testing” concept across batches of varying size. Results show that blockchain batch size used for entity classification could be reduced for certain classes (Exchange, Gambling, and eWallet) as classification rates did not vary significantly with batch size; suggesting that behavioral patterns did not change significantly over time. Mixer and Market class detection, however, can be negatively affected. A deeper analysis of Mining Pool behavior showed that models trained on recent data perform better than models trained on older data, suggesting that “typical” Mining Pool behavior may be represented better by recent data. This work provides a first step towards uncovering entity behavioral changes via temporal dissection of blockchain data.