Reviewerly/exHarmony huggingface dataset:
THE LOGIC OF INFORMATION IN STATE SPACES

Abstract State spaces are, in the most general sense, sets of entities that contain information. Examples include states of dynamical systems, processes of observations, or possible worlds. We use domain theory to describe the structure of positive and negative information in state spaces. We present examples ranging from the space of trajectories of a dynamical system, over Dunn’s aboutness interpretation of fde , to the space of open sets of a spectral space. We show that these information structures induce so-called hype models which were recently developed by Leitgeb (2019). Conversely, we prove a representation theorem: roughly, hype models can be represented as induced by an information structure. Thus, the well-behaved logic hype is a sound and complete logic for reasoning about information in state spaces. As application of this framework, we investigate information fusion. We motivate two kinds of fusion. We define a groundedness and a separation property that allow a hype model to be closed under the two kinds of fusion. This involves a Dedekind–MacNeille completion and a fiber-space like construction. The proof-techniques come from pointless topology and universal algebra.
A Neuro-Fuzzy Model for Software Defects Prediction and Analysis

Identifying defective software modules is a major factor in creating software in a cost effective and timely manner. A variety of machine learning techniques are available for predicting defective software modules. This paper investigates the effectiveness of using a fuzzy neural network in identifying faulty modules and the relative importance of the various software metrics in predicting defective modules and their role in explaining the presence of software defects. The conclusions of the work is that the model provides good accuracy but low probability of detecting software defects. However, the model does provide useful insight into the predictive and explanatory power of the various metrics affecting defect detection. The work shows that the most influential predictive metrics are those related to the measurement of program length and complexity. However, the degree of their relative influence is different for different datasets. On the explanatory side, metrics relating to program complexity, program length, and number of operators and operands are the most influential in explaining the presence of software defects. Again, their relative importance and the importance of other metrics vary for different datasets. These results are tentative and influenced to some degree by the model architectural parameters and configurations. Future work will concentrate on tuning the model and identifying a group of software characteristic that collectively can provide a good probability of defect detection.
Handwritten Tibetan Digital Words Recognition Based on improved Convolutional Neural Network

This paper proposes a kind of handwritten Tibetan digital words recognition algorithm, which based on improved convolutional neural network. The algorithm uses the convolution operations and pooling operations to fully extract the features of images. The adaptive learning rate algorithm is applied to the training process of the network, so that the network training rate can improved. Finally, the image features extracted from the neural network are input into the SVM classifier for classification and recognition. The experimental results show that the recognition rate of handwritten Tibetan digital words has been improved significantly. It can be seen that the recognition performance of this algorithm is better than the classic LeNet-5 model.
From Phrase Structure to Form

The <italic>balletto</italic> unfolds on a uniquely small scale: many <italic>balletti</italic> can be performed in less than a minute. The genre’s brevity supports a number of perceptual benefits that train listeners to attend to tonal dynamics at multiple scales. The shortest <italic>balletti</italic> lie at the perceptual limit for entraining hypermeter and within the boundary for remembering tonic. Dynamic attending theory posits that periodic cadences correspond with peaks of attention, facilitating comparison of distant harmonic events. The <italic>balletto</italic>’s repeat structure fosters a deeper knowledge of tonal and formal procedures, and repetition directs attention to larger groupings. Together, these principles enabled listeners to identify important harmonic events, compare them across broad time spans, and associate them with specific formal units. Furthermore, a comparison of Italian, English, and German <italic>balletti</italic> reveals important regional differences in tonal and harmonic norms, illustrating how English composers, especially Thomas Morley, maximally leveraged the genre’s profound perceptual benefits.
SPCTR: Sealed Auction-Based Procurement for Closest Pre-Tender with Range Validation

Over the past decades, there have existed extensive research works on the designs of the closest pre-tender procurement bidding. However, most solutions for the closest pre-tender only target at economic benefits while omitting the problem of bid privacy leakage. Moreover, existing works fail to provide approaches with adequate security and high efficiency. In this paper, for the first time, we propose SPCTR, a sealed-price auction-based procurement bidding system for the closest pre-tender with range validation. SPCTR allows a range validation for a supplier’s bid without leaking the secret bid. Besides, SPCTR achieves a sealed-price comparison with the pre-tender to find the closest pre-tender bid. Compared with previous works, SPCTR provides strong privacy protection for the bids of suppliers without sacrificing high efficiency. SPCTR is constructed based on carefully designed cryptographic tools with generality and simplicity which enable various operations on the encrypted values, and these tools can be easily applied to other contexts. We not only formally prove that SPCTR is secure against semihonest adversaries but also comprehensively analyze the efficiency. Experimental results validate that SPCTR achieves procurement bidding with light computation time and communication cost in practice.
Towards application performance fairness on clouds

In cloud computing, resource allocation is the key building block. Existing resource allocation strategies are designed for multi-tenant cases and the majority of them target resource fairness and utilization. We consider the problem of resource allocation among multiple applications that belong to a single user. In this case, instead of resource fairness, the user cares about performance fairness. We focus on memory, one of the most universal resources used by applications. We mainly face two challenges. The first one is how to characterize the performance of various applications. We carefully choose User time, the number of CPU jiffies spending on the User mode, as a representative of the application's progress to reflect the performance of the application. Through a series of processing, we make performance comparable among different applications. The other challenge is to ensure high system memory utilization. We address this challenge by designing an adaptive memory allocation algorithm that guarantees high memory utilization and performance fairness simultaneously through dynamically reallocating memory among applications. We have implemented our algorithm by developing a scheduler on a physical machine where multiple applications share resources. Experimental evaluation shows that our algorithm can achieve good performance fairness on the premise of ensuring high memory utilization with different applications and importance.
Contextual Clustering for Automated State Estimation by Sensor Networks

Through the lens of an application in space object tracking, we develop the concept of a data-aware algorithmic similarity kernel that enables clustering of partial state observations according to their source phenomena. Particularly, we consider data sets consisting of such observations made by distributed sensor networks where pairwise comparisons yield no useful association. Utilizing the data set as context, likelihoods of association for pairs are assigned by an algorithmic similarity measure that incorporates expert knowledge and domain specific heuristics through statistical analysis of higher order tuples. Spectral clustering is applied to the resulting affinity matrix to partition the data by source, where further expert analysis can better characterize the states being observed. The example application has low dimensionality and relatively simple statistical associations that make it an ideal model for illustrating the overall approach.
The Cost of a Macaroon

Macaroons are authorization credentials which have high efficiency and expressiveness that rivals public-key-based mechanisms like Simple Public Key Infrastructure (SPKI) / Simple Distributed Security Infrastructure (SDSI). Macaroons are simple and easy to deploy similar to web cookies. Additionally, they offer secure delegation via their chained hashed message authentication code (HMAC) construction which provides a construct for decentralized delegation. These characteristics are motivating factors for the adoption of macaroons; however, the modern web environment has changed since the initial concept was demonstrated on a local server and since that time there has been a broad adoption of cloud services and an expansion of mobile and embedded devices with the trend of Internet of Things (IoT).The approach utilized by macaroons has a much lower processing overhead and lower latency compared to other authorization methods used in the cloud. Our work investigates factors relating to the overhead costs of macaroon primitives. We implement a macaroons library in Python and replicate the measurements taken by Birgisson et al. in 2014. Then we evaluate the overhead of macaroon primitives and validate the prior findings regarding the cost of authorization primitives on a comparable computing platform. We also run tests with different payload sizes to assess if the prior results continue to hold true using a variety of common payload sizes. Our results provided insight into the trade offs of how efficient and expressive macaroons can be. We additionally evaluate the differences for the cost of authorization primitives across a variety of platforms to assess macaroons relative to the use of modern computer processors and modern software libraries, including ARM architecture processors to evaluate their use with IoT devices. Our results indicate that hardware and software advances have decreased the cost of using macaroons and they are efficient enough to use in low-power embedded platforms.
Modulation recognition of space-time block codes based on Fourth-order delay matrix

In order to identify the modulation mode of STBC signal in frequency selective fading channel, an improved STBC modulation recognition algorithm based on fourth-order time delay moment is proposed in the condition of single receiving antenna. This algorithm calculates the fourth-order delay moment of spacetime block code at the receiving end, and uses threshold detection method to compare the estimated value of fourth-order delay moment with the threshold interval of theoretical value, and automatically identifies the modulation mode of signal. Simulation results show that the algorithm is not affected by adverse factors such as frequency deviation and phase jitter, and there is little difference between different STBC recognition effects.
How to Position the Knob on a Aluminium Bifold Door

Although you can install the knob on any part of an aluminium bifold door, it’s recommended to place it properly for the best functionality. The ...
Proof of Data Distribution Based on Trusted Hardware

We consider a scenario in which two parties prove to a third party that communication between them occurred. This can be seen in advertisement in which the data are distributed to the user by a distributor on behalf of owner. Specifically, we propose a protocol and a modification of it to address the possibility of collusion between the distributor and user in which the owner is deceived to pay for false claim of distribution, and the possibility of the user not providing any data transfer confirmation as an acknowledgement after receiving data. In the original protocol, the distributor and user are both equipped with trusted hardware. The data are encrypted and decrypted by this trusted hardware, with a shared secret key. When decrypting the data, the user's hardware generates a proof and sends it to the distributor. This proof is used as the proof of distribution to show to the owner for payment. This prevents the collusion with the help of the trusted hardware. This protocol assumes that the user will not misbehave and always send the proof back to the distributor. To address this issue, the modified protocol uses blockchain as a public ledger for the user to publish acknowledgement of receiving encrypted data chunk, and for the distributor to publish the corresponding secret for decryption. In both the protocols, we use the blockchain technology as a method for the ID management and payment.
Deeper Attention-Based Network for Structured Data

Deep learning methods are applied into structured data and in typical methods, low-order features are discarded after combining with high-order featuresfor prediction tasks. However, in structured data, ignorance of low-order features may cause the low prediction rate. To address this issue, in this paper, deeper attention-based network (DAN) is proposed. With DAN method, to keep both low- and high-order features, attention average pooling layer was utilized to aggregate features of each order. Furthermore, by shortcut connections from each layer to attention average pooling layer, DAN can be built extremely deep to obtain enough capacity. Experimental results show DAN has good performance and works effectively.
An investigation on the feasibility of the bluetooth frequency hopping mechanism for the use as a covert channel technique

Adaptive Frequency Hopping is a mechanism included in the Bluetooth standard to minimize the effects of interference from other signals sharing the same frequency band. In this paper, several possible strategies of exploiting the frequency hopping mechanism as a covert channel are discussed. There has been some research presenting ways to make use of covert channels over Bluetooth yet none have explored frequency hopping in this context. Three groups of approaches are presented for sending hidden information by means of exploiting specific properties of the frequency hopping mechanism and the generated hopping sequence. These groups consist of strategies to transmit data hidden in protocol packets, modulated on manipulated hopping sequences or by influencing available channels through jamming, thus limiting possible hop frequencies. These approaches are compared by their bandwidth, ease of implementation as well as the detectability of a communication using these covert channels. We show that there are vast unexplored opportunities for covert communication using the adaptive frequency hopping mechanism used by Bluetooth devices.
Types of Murals

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Image Ranking with Density Trees for Google Maps

We propose an unsupervised learning technique for image ranking of photos contributed by Google Maps users. A density tree is built for each point-of-interest (POI), such as The National Mall or the Louvre. This tree is used to construct clusters, which are then ranked based on size and quality. We choose a representative image for each cluster, resulting in a ranked set of high-quality, diverse, and relevant images for each POI. We validated our algorithm in a side-by-side preference study.
A Weighted Multi-task Learning Approach for Mandarin-English Code-switching Speech Recognition

This work proposes a weighted Multi-task Learning (wMTL-DNN) approach for enhancing the performance of Mandarin-English Code-Switching Speech Recognition (MECS-SR). Focusing on Mandarin, a special language, this approach chooses two acoustic units to obtain different kinds of information for acoustic modeling. One is initial/final-based units, and the other is phoneme-based. Those two acoustic units can describe the salient acoustic and phonetic information for Mandarin. To make full use of the important information, we adopt Multi-task Learning framework based on Deep Neural Network (MTL-DNN), which can fuse those information simultaneously. To further get a just weight during transferring knowledge, we propose a weighted MTL-DNN (wMTL-DNN) to fuse these different information. Extensive experiments are carried out on LDC2015S04 and Mixed Error Rate (MER) is used as performance metric for the Code-switching Speech Recognition. Compared with the baseline and the first MECS-SR system [1] on LDC2015S04, MER of proposed approach is relatively reduced by 11.69% and 12.43%, respectively. Even relatively reduced by 11.23%, compared with the first MECS- SR system [1] added language identification task.
A Novel Multiple Hypothesis Tracking Algorithm Integrated with Detection Processing

In current multi-targets tracking system, the multiple hypotheses tracking (MHT) algorithm is considered as the preferred algorithm for data association. However, this method is straightforward, and ignores the couple relationship between detector and tracker, which may result system performance loss. In this paper, we propose a novel multiple hypothesis tracking algorithm integrated with detection processing (MHT-IDP) for multi-target tracking. It explores the idea of integrating the detector with the tracker, in which the tracker guides the detector where to search a target, and the detector provides what is discovered. Moreover, we find that the Bayesian detection threshold is lower near the predicted measurement. Simulation results demonstrate that the MHT-IDP algorithm can improve the clutter suppression effect, enhance the detection performance of the target, and achieve better performance in tracking accuracy compared with the MHT algorithm.
Part-based Convolutional Network for Visual Tracking

Recently, Convolution Neural Networks(CNNs), which provide an valuable end-to-end image representation, have been a hot topic in visual tracking. Benefiting from the receptive field and the deep structure, CNNs can extract the deep representation of the image, which can effectively solve the target deformation in the tracking process. However, because the convolution kernel of the CNN is globally shared, it will still get disturbed features and affect the robustness of the results in background clutters, illumination variation, and so on. In this paper, we propose a novel part-based convolution network for visual tracking, which incorporates the advantages of the part-based model and the CNN for a better performance. Extensive experimental results on the OTB2013 and OTB100 tracking benchmark demonstrate that the performance of our method compares competitive with some state-of-the-art trackers.
PVFE: Point-Voxel Feature Encoders for 3D Object Detection

3D object detection is a challenging problem in 3D computer vision. In this paper, a Point-Voxel Feature Encoder (PVFE) is proposed to obtain the spatial context information between points and voxels for 3D object detection in point clouds. Firstly, the point cloud is converted into 3D voxel grids to avoid loss of spatial structure information in projecting. Then, voxel-wise features are learned through PVFE, which is consisting of stacked fully connected layer. Last, voxel-wise features are processed by the Sparse Convolutional Layers and as the input of RPN which performs category prediction and bounding box regression. Experiments of car, bicyclist and pedestrian detection have been conducted respectively on the KITTI benchmark. Experimental results have clearly shown the superiority of the proposed network.
RDBMS Based Hadoop Metadata and Log Data Management Optimization

At the moment, metadata is one of the fastest growing sub-segments of enterprise data management. While metadata is growing, it is not able to keep pace with the rapid increase of Big Data projects being currently initiated by organizations. Nowadays, it refers to this as the “Big Data Gap”. This paper introduces novel approach by bringing Apache Hadoop and Relational database together to minimize the query time, resource usage, and increase the fault tolerance, and efficiency. Hadoop's metadata and log files are synchronously being migrated to the PGSQL and easily controlled through the graphical user interface. The experiment part has used 100.000's of movie rates dataset and decreased the resource usage of NameNode by giving the task of log and metadata analysis to the PGSQL. The query time in PGSQL is 1.5 times faster than Hadoop and the data format is in structured format comparing to Hadoop. Although, the technique implemented on a single node, it outperformed existing hadoop on premise and on cloud. The technique makes the metadata and log data management easier through the GUI that uses charts and graphs. The results suggest that the proposed approach performs better than existing solution and sharply decreases the usage of Big Data hardware systems and budget as well.
Audio CAPTCHA with a Few Cocktails: It’s so Noisy I Can’t Hear You

With crime migrating to the web, the detection of abusive robotic behaviour is becoming more important. In this paper, we propose a new audio CAPTCHA construction that builds upon the Cocktail Party problem (CPP) to detect robotic behaviour. We evaluate our proposed solution in terms of both performance and usability. Finally, we explain how to deploy such an acoustic CAPTCHA in the wild with strong security guarantees.
Bivariate Risk Measures and Stochastic Orders

Bivariate value-at-risks and bivariate average value-at-risks are defined with copula functions, and stochastic orders are induced from these bivariate value-at-risks and bivariate average value-at-risks. Value-at-risk order is almost equivalent to the first-order stochastic dominance, however it is made clear that average value-at-risk order is weaker than the second-order stochastic dominance and it has similar properties. We find that average value-at-risk order is an important criterion as a stochastic order in multi-object case-based reasoning with risk aversity.
A Principal Component Analysis Approach for Embedding Local Symmetries into Deep Learning Algorithms

Building robust-by-design Machine Learning algorithms is key for critical tasks such as safety or military applications. By leveraging on the ideas developed in the context of building invariant Support Vectors Machines, this paper introduces a convenient methodology for embedding local Lie groups symmetries into Deep Learning algorithms by performing a Principal Component Analysis on the corresponding Tangent Covariance Matrix. The projection of the input data onto the principal directions leads to a new data representation which allows singling out the components conveying the semantic information useful to the considered algorithmic task while reducing the dimension of the input manifold. Besides, our numerical testing emphasizes that, although less efficient than using Group-Convolutional Neural Networks as only dealing with local symmetries, our approach does improve accuracy and robustness without introducing significant computational overhead. Performance improvements up to 5% were obtained for low capacity algorithms, making this approach of particular interest for the engineering of safe embedded Artificial Intelligence systems.