Reviewerly/exHarmony huggingface dataset:
Whisper

Encrypted voice-over-IP (VoIP) communication often uses variable bit rate (VBR) codecs to achieve good audio quality while minimizing bandwidth costs. Prior work has shown that encrypted VBR-based VoIP streams are vulnerable to re-identification attacks in which an attacker can infer attributes (e.g., the language being spoken, the identities of the speakers, and key phrases) about the underlying audio by analyzing the distribution of packet sizes. Existing defenses require the participation of both the sender and receiver to secure their VoIP communications.
Enhancement of Low-Light Image Based on Wavelet U-Net

In computer vision, low-light image enhancement has always been a challenging task caused by more lower signal to noise ratio. Some methods have been proposed to enhance the low-light image using fully convolution network. Using u-net as backbone, we introduce wavelet transform to conduct down-sampling and up-sampling operations. In order to recover more details, perceptual loss has been used to optimize the network parameters. Experiments show that our model can get better performance than the existing methods. We find that wavelet transform effectively improve the quality of low-light image enhancement.
Content based image retrieval using fusion of multilevel bag of visual words

Content based image retrieval (CBIR) is the art of finding visually and conceptually similar pictures to the given query picture. Usually, there is a semantic gap between low-level image features and high-level concepts perceived by viewers. Although features such as intensity and color enforce a good distinction between the images in terms of greater detail, they convey little semantic information. Therefore, employing higher-level features such as properties of regions and objects within the image could improve the retrieval performance. In this study, features are extracted at the pixel, region, object, and concept levels. The fusion step concatenates the four feature vectors and maps it to a lower-dimensional space using auto-encoders. The experiments confirm the efficiency of the proposed method over the individual feature groups and also the state of the art methods.
Designing Supervisory Control Systems for NPP: New Opportunities

The paper presents the new capabilities of modern technologies for the design of modern systems for automated control systems of nuclear power plants.
Generation of accessible sets for a class of quantum spin networks

In this paper, we consider the modeling of dynamical spin network systems. The system Hamiltonian governs the evolution of the network system and shows the structure of how the element systems are coupled. Probes are employed to measure a set of spins of the network. For a variety of applications, the state space model is a useful way to describe the system dynamics. One important task in establishing a state space model is to obtain an accessible set containing all the operators coupled to the measurement operators. We provide analytic results on simplifying the process of generating accessible sets. An example is provided to demonstrate the generation of an accessible set under a certain measurement scheme.
Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition

In this work, we aim to re-rank the n-best hypotheses of an automatic speech recognition system by punishing the sentences which have words that are semantically different from the context and rewarding the sentences where all words are in semantical harmony. To achieve this, we proposed a topic similarity score that measures the difference between topic distribution of words and the corresponding sentence. We also proposed another word-discourse score that quantifies the likeliness for a word to appear in the sentence by the inner production of word vector and discourse vector. Besides, we used the latent semantic marginal and a variation of log bi-linear model to get the sentence coordination score. In addition we introduce a fallibility weight, which assists the computation of the sentence semantically coordination score by instructing the model to pay more attention to the words that appear less in the hypotheses list and we show how to use the scores and the fallibility weight in hypotheses rescoring. None of the rescoring methods need extra parameters other than the semantic models. Experiments conducted on the Wall Street Journal corpus show that, by using the proposed word-discourse score on 50-dimension word embedding, we can achieve 0.29% and 0.51% absolute word error rate (WER) reductions on the two testsets.
A System for Scalable Decentralized Random Number Generation

Generating public randomness has been significantly demanding and also challenging, especially after the introduction of the Blockchain Technology. Lotteries, smart contracts, and random audits are examples where the reliability of the randomness source is a vital factor. We demonstrate a system of random number generation service for generating fair, tamper-resistant, and verifiable random numbers. Our protocol together with this system is an R&D project aiming at providing a decentralized solution to random number generation by leveraging the blockchain technology along with long-lasting cryptographic primitives including homomorphic encryption, verifiable random functions. The system decentralizes the process of generating random numbers by combining each party's favored value to obtain the final random numbers. Our novel idea is to force each party to encrypt his contribution before making it public. With the help of homomorphic encryption, all encrypted contribution can be combined without performing any decryption. The solution has achieved the properties of unpredictability, tamper-resistance, and public-verifiability. In addition, it only offers a linear overall complexity with respect to the number of parties on the network, which permits great scalability.
Long-Term Knowledge Distillation of Visual Place Classifiers

Most of the current state-of-the-art frameworks for cross- season visual place recognition (CS-VPR) focus on domain adaptation (DA) to a single specific season. From the viewpoint of long-term CS-VPR, such frameworks do not scale well to sequential multiple domains (e.g., spring → summer → autumun → winter → ⋯). The goal of this study is to develop a novel long-term ensemble learning (LEL) framework that allows for a constant cost retraining in long- term sequential-multi-domain CS-VPR (SMD-VPR), which only requires the memorization of a small constant number of deep convolutional neural networks (CNNs) and can retrain the CNN ensemble of every season at a small constant time/space cost. We frame our task as the multi-teacher multi-student knowledge distillation (MTMS-KD), which re- cursively compresses all the previous season's knowledge into a current CNN ensemble. We further address the issue of teacher-student-assignment (TSA) to achieve a good general- ization/specialization tradeoff. Experimental results on SMD-VPR tasks validate the efficacy of the proposed approach.
Computing Focus Time of Paragraph Using Deep Learning

Time is a main part of any text that shows value of the document. We have two type of documents that are temporal and atemporal characters. In this paper we explain problem of focus time in historical documents using convolutional neural network (CNN) which shows the time period of text contents and it measure out the document creation time. We used to different type of documents that one is Wikipedia documents and another is web news texts. We evaluate the advantages of our method on various text documents that related to historical events in defined timelines. We used different methods for evaluating focus time of paragraph. To find the relationship between words in document we used different simulation methods and word 2vec system to get more detail results of words. Our proposed approach contains focus time of paragraphs in text documents using deep learning process.
Internal Dictionary Matching

We introduce data structures answering queries concerning the occurrences of patterns from a given dictionary $$\mathsf {D}$$ in fragments of a given string T of length n. The dictionary is internal in the sense that each pattern in $$\mathsf {D}$$ is given as a fragment of T. This way, $$\mathsf {D}$$ takes space proportional to the number of patterns $$d=|\mathsf {D}|$$ rather than their total length, which could be $$\varTheta (n\cdot d)$$ . In particular, we consider the following types of queries: reporting and counting all occurrences of patterns from $$\mathsf {D}$$ in a fragment $$T[i \mathinner {.\,.}j]$$ and reporting distinct patterns from $$\mathsf {D}$$ that occur in $$T[i \mathinner {.\,.}j]$$ . We show how to construct, in $$O((n+d) \log ^{O(1)} n)$$ time, a data structure that answers each of these queries in time $$O(\log ^{O(1)} n+| output |)$$ . The case of counting patterns is much more involved and needs a combination of a locally consistent parsing with orthogonal range searching. Reporting distinct patterns, on the other hand, uses the structure of maximal repetitions in strings. Finally, we provide tight—up to subpolynomial factors—upper and lower bounds for the case of a dynamic dictionary.
Quality Improvement for UML and OCL Models Through Bad Smell and Metrics Definition

Detecting and fixing software quality issues early in the design phase is indispensable for a successful project applying model-based techniques. This paper presents an extension of the tool USE (UML-based Specification Environment) with features for (a) reflective model queries and model exploration, (b) metric measurement, (c) smell detection, and (d) quality assessment with metrics. The newly added functionalities can be fine-tuned by designers, are closely related and can be applied together interactively in order to help designers to achieve better models.
iConSnap: An Incremental Continuous Snapshots System for Virtual Machines

The reliability of data and services hosted on a virtual machine (VM) is a top concern in cloud environments. The Continuous Snapshots can reduce the data loss in case of failures and thus is prevailing for protecting long-running systems. However, existing methods suffer from long VM downtime, long snapshot interval and significant performance loss. In this article, we present iConSnap, a system designed to take fine-grained continuous snapshots of virtual machines without compromising VM performance. First, iConSnap adopts the copy-on-write (COW) mechanism to save the memory pages on-demand, and thus decreases the VM downtime to about 200 milliseconds. Second, we extend the idea of COW and propose a lazily incremental approach to save the delta data between two successive snapshots only once, thereby reducing the snapshot duration and snapshot data a lot. Third, we propose a scheduling mechanism to mitigate the VM performance penalty issue. Last, we introduce a method combined of compression and time-aware multi-granularity reclamation strategy to reduce the storage costs without losing performance and availability. We implement iConSnap on QEMU/KVM and evaluate it through a set of experiments. The experimental results show that iConSnap outperforms existing approaches in terms of VM downtime, snapshot duration, storage costs and VM performance.
Autonomous Robot Photographer System based on Aesthetic Composition Evaluation using Yohaku

In this study, we aim to propose a robot photographer system which could take photographs for people with high aesthetic values by searching for the optimal photographing viewpoint autonomously. Aesthetic composition evaluation and observing viewpoint selection are two main issues which should be considered to construct this system. Different from conventional composition evaluation methods which are following some well-known rules such as rule of thirds and diagonal rule, we focus on yohaku of photographs, which means the perceptual empty space in Japanese, to help to construct an aesthetic composition more flexibly. To achieve this purpose, we first propose the yohaku detection method for actual photographs. Then aesthetic composition evaluation will be executed from the viewpoint of visual balance between ordinary targets and yohaku. Finally, using the viewpoint selection method called Composition Map, autonomous photographing will be achieved by evaluating the aesthetic value of scenes with our system.
Detecting False Information in Medical and Healthcare Domains: A Text Mining Approach

In recent years, a lot of false information in medical and healthcare domains has emerged and spread over the Internet. Such false information has become a big risk to public health and safety. This study investigates this problem by analyzing data collected from two fact-checking websites, 416 medical claims from Snopes.com and 1,692 healthcare-related statements from PolitiFact.com. Topic analysis reveals frequent words and common topics occurring in these claims spread online. Furthermore, using text-mining and machine-learning techniques, this study builds prediction models for detecting false information and shows promising performance. Several textual and source features are identified as good indicators for true or false information in medical and healthcare domains.
Position, Size and Orientation Estimation of Ground Obstacles in Sense and Avoid

This article is the improvement of the author’s previous work which considered position and size estimation of steady ground obstacles applying disc projection model and a constant velocity straight trajectory assumption. In case of square objects (trucks, buildings) the disc projection model can be a very rough approximation and so its improvement is required. This paper presents a method to estimate the orientation of the square object and applies a line projection model which gives more precise size and position estimates. The formulas of the previous work were also improved to consider non-constant velocity non-straight trajectories but tested only with constant velocity and straight trajectory assumptions to first prove applicability in a simpler case and because of the limited length of the paper.
Automatic Pitch Accent Detection Using Long Short-Term Memory Neural Networks

Prosody detection is gaining increasingly popularity in the domain of prosody research because of its significance in Text to Sound, Computer-aided pronunciation training (CAPT), etc. Pitch accent is an important part of prosody and many recognition models of both static and dynamic have been investigated for automatic labeling it. Recently, artificial neural networks, especially Recurrent Neural Networks (RNNs) have been applied in pitch accent detection. However, traditional recurrent neural networks are unable to learn and remember over long sequences due to the issue of back-propagated error decay. To solve this problem, this paper investigates the use of Long Short-Term Memory (LSTM) neural networks for automatic pitch accent detection. This paper encodes lexical and syntactic features as binary variables and uses syllable-based acoustic features including syllable duration, syllable energy, features related to the fundamental frequency. Our experimental results show that LSTM-RNNs for pitch accent detection achieves an accuracy of 89.0%, which is better than the results of using classical detection methods by about 83.2%.
A Single Image Dehazing Algorithm Based on Cycle-GAN

Due to the effect of atmospheric light scattering, the quality of images taken under haze weather conditions will be seriously degraded. These characteristics affect the judgment and extraction of image features and reduce the application value of images. Image dehazing is therefore a necessary step in many computer vision tasks. In this paper, an end-to-end image dehazing algorithm Dehaze-GAN based on Cycle-GAN is proposed. In order to ensure that the structure of the images before and after dehazing is basically the same, the algorithm adds structure consistency loss on the basis of Cycle-GAN. The input of Dehaze-GAN is a hazy image and the output is a clean image. Dehaze-GAN is trained by a large number of hazy images and their corresponding clean images. The experimental results show that Dehaze-GAN is superior to other dehazing algorithms in both PSNR and SSIM dehazing performance indicators.
Modelling Contexts for Interactions in Dynamic Open-World Scenarios

Intelligent Interactive Systems work well in well defined contexts. Therefore, current research focuses on developing specialised systems for specific tasks. However, real life situations violate these restrictions. Even with a specialised task focus Intelligent Interactive Systems still need to be able to react in a meaningful way in real world situations. In this paper, we propose a set of context factors to determine context and a coarse dependency structure for predicting behaviour. We argue that with such a limited set of context factors it will be possible to model context-specific behaviours for different contexts. Also, it can serve as a basis to determine the complexity of a situation.
Trace element associations in magnetite and hydrothermal pyrite from the Geita Hill gold deposit, Tanzania

Gold mineralization in the Geita Hill deposit is associated with pyrite formed along microfracture networks and sulfidation fronts together with K-feldspar and biotite. The sulfidation fronts are best developed in magnetite-bearing ironstone. The gold is present mainly as electrum and gold tellurides along grain boundaries, and as inclusions in pyrite, quartz, biotite and K-feldspar. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) analyses of pyrite and magnetite grains reflect complex fluid-host rock interactions. Magnetite textures and chemistry change with alteration intensity, indicating the progression of the alteration front into the host rock. Pyrite textures are uniform across all rock types and reflect late-tectonic growth linked to multi-staged infiltration of hydrothermal fluids. Trace element distribution patterns in pyrite are locally complex and influenced by host rock chemistry. Gold distribution patterns in pyrite correlate closely with Te, Ag, Bi and Pb, indicating that gold occurs in micro- and nano-inclusions of telluride minerals. This is especially so for gold in quartz veins, whereas gold in ironstone and diorite also occurs as electrum with an average Au/Ag ratio of 0.41. As, Co and Ni in pyrite are lattice bound and occur in high concentrations in ironstone and diorite where they show characteristic growth zoning patterns. Pyrite in quartz veins has As, Co and Ni concentrations that are low and variable. Cr, Cu, Mo, Mn and Zn are present in all rock types in isolated inclusions in pyrite grains, whilst Pb, Bi and Sb occur in more dispersed patches of fine clustered inclusions. The Se content in pyrite is typical for Archean gold deposits, and reflects an average temperature of ~340 °C for the mineralizing fluid. The Co/Ni ratio of pyrite grains varies between 0 and 5.2 in ironstone and diorite, and most likely reflects the equilibration Co/Ni ratio of the host rock. The Co/Ni ratio of pyrite grains in quartz veins varies between 1 and 12, and is consistent with a magmatic-hydrothermal origin for the ore fluid. Trace element distribution patterns in magnetite and pyrite indicate that As, Ni, Co, Cr, Mn and Cu were mostly locally derived, and remobilised into the pyrite during sulfidation of the host rock. The concentrations of these elements are strongly lithologically controlled, and they are not consistently incorporated into the pyrite after initial stages of growth. Au, Ag, Te, Bi and Pb were externally derived, and closely correlate in all varieties of pyrite as well as strongly altered magnetite. The alteration footprint of the Geita Hill deposit is limited in extent, and does not involve As and Sb that are typically enriched in Archaean lode-gold systems. Instead, Te and Bi are most characteristic for the deposit and could be of use as path finder elements together with altered magnetite grains.
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles

Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory's well-known "folk theorem." There is a cost, however, to a player for generating these disincentives. In this work, we seek to minimize this cost by computing a "Stackelberg punishment," in which the player selects a behavior that sufficiently punishes the other player while maximizing its own score under the assumption that the other player will adopt a best response. This idea generalizes the concept of a Stackelberg equilibrium. Known efficient algorithms for computing a Stackelberg equilibrium can be adapted to efficiently produce a Stackelberg punishment. We demonstrate an application of this idea in an experiment involving a virtual autonomous vehicle and human participants. We find that a self-driving car with a Stackelberg punishment policy discourages human drivers from bullying in a driving scenario requiring social negotiation.
XOR Binary Gravitational Search Algorithm

In this paper, an XOR binary gravitational search algorithm is introduced. Gravitational search algorithms, a physics inspired optimization algorithm, have previously been successfully applied to different real-valued optimization problems. In their binary version, the definition of a velocity vector changes to probability of change in corresponding dimensions. However, analysis shows that existing velocity vector update equation for binary gravitational search algorithm do not direct the particle towards better particle in certain cases. To apply this algorithm to binary optimization problems we introduce an XOR operator in the acceleration term. After a mathematical comparison to existing binary gravitational search algorithms it is shown that the proposed modification complies more with the definition of change in each dimension. Extensive simulations are performed showing the superiority of the proposed algorithm over other existing algorithms such as binary particle swarm optimization and an existing version of binary gravitational search algorithm.
Demo Abstract: MirrorMatch: Real-Time Detection of Repetitive Movements using Smartphone Camera

MirrorMatch is a motion tracking system that provides real-time movement analysis with the use of a mobile camera. It allows the user to evaluate their own exercises over time and gauge their progress at a lower cost than traditional methods. MirrorMatch is built upon commercial off-the-shelf smartphone cameras and image processing techniques. Unlike existing approaches that use wearable sensors, MirrorMatch offers a cost-effective and scalable solution to make finegrained movement tracking more accessible.
Serverless-Enabled Permissioned Blockchain for Elastic Transaction Processing

To overcome the performance limits of purely decentralized blockchains, off-chaining models for compute and storage have been studied recently. Serverless computing offers increased elasticity and scalability in executing microservices in event-driven manner, making it proper fit for off-chain computation tasks. In this work, we present a serverless-enabled off-chaining model for permissioned blockchains with some noticeable early results.