Reviewerly/exHarmony huggingface dataset:
Deep Learning Bidirectional LSTM based Detection of Prolongation and Repetition in Stuttered Speech using Weighted MFCC

Stuttering is a neuro-development disorder during which normal speech flow is not fluent.Traditionally Speech-Language Pathologists used to assess the extent of stuttering by counting the speech disfluencies manually.Such sorts of stuttering assessments are arbitrary, incoherent, lengthy, and error-prone.The present study focused on objective assessment to speech disfluencies such as prolongation and syllable, word, and phrase repetition.The proposed method is based on the Weighted Mel Frequency Cepstral Coefficient feature extraction algorithm and deep-learning Bidirectional Long-Short term Memory neural network for classification of stuttered events.The work has utilized the UCLASS stuttering dataset for analysis.The speech samples of the database are initially preprocessed, manually segmented, and labeled as a type of disfluency.The labeled speech samples are parameterized to Weighted MFCC feature vectors.Then extracted features are inputted to the Bidirectional-LSTM network for training and testing of the model.The effect of different hyper-parameters on classification results is examined.The test results show that the proposed method reaches the best accuracy of 96.67%, as compared to the LSTM model.The promising recognition accuracy of 97.33%, 98.67%, 97.5%, 97.19%, and 97.67% was achieved for the detection of fluent, prolongation, syllable, word, and phrase repetition, respectively.
Game AI Competitions: Motivation for the Imitation Game-Playing Competition

Games have played crucial role in advancing research in Artificial Intelligence and tracking its progress.In this article, a new proposal for game AI competition is presented.The goal is to create computer players which can learn and mimic the behavior of particular human players given access to their game records.We motivate usefulness of such an approach in various aspects, e.g., new ways of understanding what constitutes the human-like AI or how well it fits into the existing game production workflows.This competition may integrate many problems such as learning, representation, approximation and compression of AI, pattern recognition, knowledge extraction etc.This leads to multi-directional implications both on research and industry.In addition to the proposal, we include a short survey of the available game AI competitions.
Knowledge Graph Completion for the Chinese Text of Cultural Relics Based on Bidirectional Encoder Representations from Transformers with Entity-Type Information

Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale manually labelled data, which greatly increases manual labour. In order to alleviate the scarcity of labelled data in the field of cultural relics and capture the rich semantic information of entities, this paper proposes a model based on the Bidirectional Encoder Representations from Transformers (BERT) with entity-type information for the knowledge graph completion of the Chinese texts of cultural relics. In this work, the knowledge graph completion task is treated as a classification task, while the entities, relations and entity-type information are integrated as a textual sequence, and the Chinese characters are used as a token unit in which input representation is constructed by summing token, segment and position embeddings. A small number of labelled data are used to pre-train the model, and then, a large number of unlabelled data are used to fine-tune the pre-training model. The experiment results show that the BERT-KGC model with entity-type information can enrich the semantics information of the entities to reduce the degree of ambiguity of the entities and relations to some degree and achieve more effective performance than the baselines in triple classification, link prediction and relation prediction tasks using 35% of the labelled data of cultural relics.
Phase-Preserving Linear Amplifiers Not Simulable by the Parametric Amplifier

It is commonly accepted that a parametric amplifier can simulate a phase-preserving linear amplifier regardless of how the latter is realized [C. M. Caves et al., Phys. Rev. A 86, 063802 (2012)]. If true, this reduces all phase-preserving linear amplifiers to a single familiar model. Here we disprove this claim by constructing two counterexamples. A detailed discussion of the physics of our counterexamples is provided. It is shown that a Heisenberg-picture analysis facilitates a microscopic explanation of the physics. This also resolves a question about the nature of amplifier-added noise in degenerate two-photon amplification.
Dimension Relation Modeling for Click-Through Rate Prediction

Embedding mechanism plays an important role in Click-Through-Rate (CTR) prediction. Essentially, it tries to learn a new feature space with some learned latent properties as the basis, and maps the high dimensional and categorical raw data to dense, rich and expressive representations, i.e., the embedding features. Current researches usually focus on learning the interactions through operations on the whole embedding features without considering the relations among the learned latent properties. In this paper, we find it has clear positive effects on CTR prediction to model such relations and propose a novel Dimension Relation Module (DRM) to capture them through dimension recalibration. We show that DRM can improve the performance of existing models consistently and the improvements are more obvious when the embedding dimension is higher. We further boost Field-wise and Element-wise embedding methods with our DRM and name this new model FED network. Extensive experiments demonstrate that FED is very powerful in CTR prediction task and achieves new state-of-the-art results on Criteo, Avazu and JD.com datasets.
NoPKI - a Point-to-Point Trusted Third Party Service Based on Blockchain Consensus Algorithm

The increasingly interconnected network results in the change of information flow between users and service providers, hence there is an emerging need for building a secure channel for connections. Currently, Public Key Infrastructure System (shortly PKI), the hierarchical trust relationship system, is the most widely used cornerstone technology to help secure the communication channels. However, PKI recently concerns the users due to various security breaches, i.e., the compromised PKI allows attackers to issue any valid keys to the victim and decrypt any secure connections within the system. In this paper, we design a decentralized PKI system, called NoPKI, by leveraging the blockchain technology. The system has multiple small and trusted groups called the neighborhood, which can be formed dynamically. These neighbors are the players in each neighborhood, and each transaction in the PKI system (including registration, revocation, and validation) requires witnesses of neighbors. In the evaluation, we implement our system and validate that our system ensures not only long-term accessibility but also the certificate security and public audit-ability.
Method to eliminate thermal disturbance errors in digital image correlation measurement based on projection speckle

Digital image correlation (DIC) technology is an optical measurement method of material strain displacement based on visible light illumination. With the increasing application of DIC technology in the field of high-temperature measurement, however, the effect of thermal disturbance on measurement accuracy becomes more and more serious. To solve this problem, a method to eliminate thermal disturbance in material measurements based on projection speckle is proposed. First, the gray surface information of two different colors is assigned to the surface of the test piece by artificial splashing and projector projection. The pictures are collected using a color camera, and the channels are separated for each frame of the picture. After that, the displacement field recorded by different channels can be obtained by DIC calculation so the thermal disturbance error can be separated from the real displacement and eliminated. Subsequently, an experimental system is built. Finally, the corrected results are compared with the true displacement value of the stage. The results show that the two sets of values are highly consistent, which verifies the feasibility and accuracy of the proposed method.
Individual Differences in Font Preference &amp; Effectiveness as Applied to Interlude Reading in the Digital Age

In the age of pervasive reading on digital devices, incredible opportunities for customized interfaces abound. We consider how personalizing body text font can improve reading outcomes for adult readers. We present results of large-scale Interlude Reading experiments run on 386 crowdsourced participants, whereby we tested 16 body text fonts and measured impacts on font preference and reading speed. We define “Interlude Reading,” nestled between glanceable and long-form reading, as the form of reading that occurs in short interludes and is common in the mobile context. Our studies controlled for participants’ interest and familiarity with reading passages, familiarity with font families, and font size - via a perceptually-based font size normalization technique. While past work has considered how these factors affect reading in isolation, we present the first study that combines these factors under a single experimental methodology. First, our results show that normalizing a font’s size affects reading speed and font preferences. Second, familiarity with a font predicts neither preference nor effectiveness of a font. Third, people do not know what is good for them: despite 80% of people believing their most preferred font would be their most effective for reading, this was only true 18% of the time. Fourth, and most surprisingly, a simple change in font yielded a reading speed gain of 38 words per minute on average, and 93 WPM for our top quartile of participants (equivalent to eight pages an hour) while comprehension remained similarly high. With these results, we put forward (1) a methodology for running large-scale controlled reading studies, (2) a computational toolkit of crowdsourcing experiments, and (3) a list of concrete recommendations about favorable and effective fonts, and font normalization strategies. Moreover, the potential impacts on individual reading efficacy highlighted here demonstrate a need to further exploit the personalization of text formats.
BISM: Bytecode-Level Instrumentation for Software Monitoring

BISM (Bytecode-level Instrumentation for Software Monitoring) is a lightweight Java bytecode instrumentation tool which features an expressive high-level control-flow-aware instrumentation language. The language follows the aspect-oriented programming paradigm by adopting the joinpoint model, advice inlining, and separate instrumentation mechanisms. BISM provides joinpoints ranging from bytecode instruction to method execution, access to comprehensive context information, and instrumentation methods. BISM runs in two modes: build-time and load-time. We demonstrate BISM effectiveness using two experiments: a security scenario and a general runtime verification case. The results show that BISM instrumentation incurs low runtime and memory overheads.
Intensity equations for birefringent spin lasers

Semiconductor spin lasers are distinguished from their conventional counterparts by the presence of spin-polarized carriers. The transfer of angular momentum of the spin-polarized carriers to photons provides important opportunities for the operation of lasers. With the injection of spin-polarized carriers, which lead to the circularly polarized light, the polarization of the emitted light can be changed an order of magnitude faster than its intensity. This ultrafast operation of spin lasers relies on a large birefringence, usually viewed as detrimental in spin and conventional lasers. We introduce a transparent description of spin lasers using intensity equations which elucidate the influence of birefringence on the intensity and polarization modulation of lasers. Unlike commonly describing the role of birefringence on laser dynamics by employing complex quantities, our approach is simpler, because it is relying on real quantities and allowing analytical solutions. While intensity modulation is independent of birefringence, for polarization modulation an increase in birefringence directly increases the resonant frequency. Our results for dynamical operation of lasers provide a guide for their spin-dependent response and spintronic applications beyond magnetoresistance.
K-MEANS CLUSTERING FOR EGG EMBRYO'S DETECTION BASED-ON STATISTICAL FEATURE EXTRACTION APPROACH OF CANDLING EGGS IMAGE

This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The statistical feature extraction applied is the Gray Level Co-occurrence Matrix (GLCM) method, which consists of 6 features, namely Energy, Contrast, Entropy, Variance, Correlation, and Homogeneity. The results of feature extraction (6 features) are grouped by the K-means Clustering method. The clustering process uses Euclidean distance calculations to determine the proximity of features. The results of grouping and testing give the best average results with an accuracy of ≈ 74% from several test samples.
Profiling Dilithium Digital Signature Traces for Correlation Differential Side Channel Attacks

A significant concern for the candidate schemes of the NIST postquantum cryptography standardization project is the protection they support against side-channel attacks. One of these candidate schemes currently in the NIST standardization race is the Dilithium signature scheme. This postquantum signature solution has been analyzed for side channel attack resistance especially against timing attacks. Expanding our attention on other types of side-channel analysis, this work is focused on correlation based differential side channel attacks on the polynomial multiplication operation of Dilithium digital signature generation. In this paper, we describe how a Correlation Power Attack should be adapted for the Dilithium signature generation and describe the attack process to be followed. We determine the conditions to be followed in order for such an attack to be feasible, (isolation of polynomial coefficient multiplication inpower traces) and we create a power trace profiling paradigm for the Dilithium signature scheme executed in embedded systems to showcase that the conditions can be met in practice. Expanding the methodology of recent works that mainly use simulations for power trace collection, in this paper, power trace capturing and profiling analysis of the signature generation process was succesfully done on a, noisy, Commercial off-the-shelf ARM Cortex-M4 embedded system.
Improving Local Identifiability in Probabilistic Box Embeddings

Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily, allowing them to naturally represent calibrated probability distributions. The benefits of geometric embeddings also introduce a problem of local identifiability, however, where whole neighborhoods of parameters result in equivalent loss which impedes learning. Prior work addressed some of these issues by using an approximation to Gaussian convolution over the box parameters, however, this intersection operation also increases the sparsity of the gradient. In this work, we model the box parameters with min and max Gumbel distributions, which were chosen such that space is still closed under the operation of the intersection. The calculation of the expected intersection volume involves all parameters, and we demonstrate experimentally that this drastically improves the ability of such models to learn.
“When and Where Do You Want to Hide?” – Recommendation of Location Privacy Preferences with Local Differential Privacy

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference.
WebJShrink: a web service for debloating Java bytecode

As software projects grow in complexity, they come packaged with under-utilized libraries and therefore become bloated. Though several software debloating tools exist, none of them help developers gain insights into how under-utilized those libraries are nor help developers build confidence in the behavior preservation of software after debloating. To bridge this gap, we developed WebJShrink, a visual analytics tool for analyzing and pruning bloated software projects. WebJShrink is built on JShrink which uses static and dynamic reachability analysis to determine the extent of software bloat. WebJShrink provides rich visualizations of the bloat lurking within a target project's internal structure. It then removes unused features, and returns a safer, slimmer variant of the software project. To illustrate the target project's behavior preservation, WebJShrink examines the debloated software with its JUnit tests and visualizes the test results. In evaluating WebJShrink against 26 real world systems, we found WebJShrink could reduce software size by up to 42%, 11% on average, while still passing 100% of unit tests after debloating. We provide a video demonstrating WebJShrink at https://youtu.be/yzVzcd-MJ1w.
Assessing the Sensitivity and Accuracy of the MyShake Smartphone Seismic Network to Detect and Characterize Earthquakes

MyShake harnesses private/personal smartphones to build a global seismic network. It uses the accelerometers embedded in all smartphones to record ground motions induced by earthquakes, returning recorded waveforms to a central repository for analysis and research. A demonstration of the power of citizen science, MyShake expanded to 6 continents within days of being launched, and has recorded 757 earthquakes in the first 2 years of operation. The data recorded by MyShake phones has the potential to be used in scientific applications, thereby complementing current seismic networks. In this paper: (1) we report the capabilities of smartphone sensors to detect earthquakes by analyzing the earthquake waveforms collected by MyShake. (2) We determine the maximum epicentral distance at which MyShake phones can detect earthquakes as a function of magnitude. (3) We then determine the capabilities of the MyShake network to estimate the location, origin time, depth and magnitude of earthquakes. In the case of earthquakes for which MyShake has provided 4 or more phases (21 events), either P- or S-wave signals, and has an azimuthal gap less than 180 degrees, the median location, origin time and depth errors are 2.7 km, 0.2 s, and 0.1 km respectively relative to USGS global catalog locations. Magnitudes are also estimated and have a mean error of 0.0 and standard deviation 0.2. These preliminary results suggest that MyShake could provide basic earthquake catalog information in regions that currently have no traditional networks. With an expanding MyShake network, we expect the event detection capabilities to improve and provide useful data on seismicity and hazards.
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes. A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network’s accuracy. However, previous post-hoc calibration techniques work only with simple calibration functions, potentially lacking sufficient representation to calibrate the complex function landscape of deep networks. In this work, we aim to learn general post-hoc calibration functions that can preserve the top-k predictions of any deep network. We call this family of functions intra order-preserving functions. We propose a new neural network architecture that represents a class of intra order-preserving functions by combining common neural network components. Additionally, we introduce order-invariant and diagonal sub-families, which can act as regularization for better generalization when the training data size is small. We show the effectiveness of the proposed method across a wide range of datasets and classifiers. Our method outperforms state-of-the-art post-hoc calibration methods, namely temperature scaling and Dirichlet calibration, in several evaluation metrics for the task.