Reviewerly/exHarmony huggingface dataset:
One-Class Classification with Application to Forensic Analysis

Summary The analysis of broken glass is forensically important to reconstruct the events of a criminal act. In particular, the comparison between the glass fragments found on a suspect (recovered cases) and those collected at the crime scene (control cases) may help the police to identify the offender(s) correctly. The forensic issue can be framed as a one-class classification problem. One-class classification is a recently emerging and special classification task, where only one class is fully known (the so-called target class), whereas information on the others is completely missing. We propose to consider Gini's classical transvariation probability as a measure of typicality, i.e. a measure of resemblance between an observation and a set of well-known objects (the control cases). The aim of the proposed transvariation-based one-class classifier is to identify the best boundary around the target class, i.e. to recognize as many target objects as possible while rejecting all those deviating from this class.
On Nonparametric Estimation of the Fisher Information

This paper considers a problem of estimation of the Fisher information for location from a random sample of size n. First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived. Second, a new estimator, termed clipped estimator, is proposed. The new estimator is shown to have superior rates of convergence as compared to the Bhattacharya estimator, albeit with different regularity conditions. Third, both of the estimators are evaluated for the practically relevant case of a random variable contaminated by Gaussian noise. Moreover, using Brown's identity, which relates the Fisher information to the minimum mean squared error (MMSE) in Gaussian noise, a consistent estimator for the MMSE is proposed.
Anonymous Model Pruning for Compressing Deep Neural Networks

Many deep neural network compression algorithms need to fine-tune on source dataset, which makes them unpractical when the source datasets are unavailable. Although data-free methods can overcome this problem, they often suffer from a huge loss of accuracy. In this paper, we propose a novel approach named Anonymous-Model Pruning (AMP), which seeks to compress the network without the source data and the accuracy can be guaranteed without too much loss. AMP compresses deep neural networks via searching pruning rate automatically and fine-tuning the compressed model under the teacher-student diagram. The key innovations are that the pruning rate is automatically determined, and the fine-tuning process is under the guidance of uncompressed network instead of labels. Even without the source dataset, compared with existing pruning methods, our proposed method can still achieve comparable accuracy with similar pruning rate. For example, for ResNet50, our AMP method only incur 0.76% loss in top-1 accuracy with 32.72% pruning rate.
Uncertainty Velocity Obstacle Avoidance for sUAS Trajectory Planning in a 2D Plane

To guarantee safe autonomous navigation of multiple small unmanned aircraft systems (sUAS) operating within a limited airspace, collision avoidance algorithms must be robust to uncertainties in aircraft states. The velocity obstacle (VO) concept is a popular avoidance algorithm which uses a collision cone to effectively determine if two objects will collide in the near future. The VO method is a reactive algorithm that allows for avoidance of dynamic obstacles. This paper proposes a novel approach, the uncertainty velocity obstacle (UVO) method, to enhance the decentralized VO collision avoidance method by addressing uncertainties in the position and velocity of moving obstacles. A scenario is presented to illustrate the utility of this method for an sUAS encountering other cooperating vehicles. In this scenario the vehicles use global positioning system (GPS) messages to communicate position and velocity between cooperative vehicles. Each vehicle uses these states in a constant-jerk Kalman filter to estimate other vehicles' positions and velocities. Numerical simulations show that UVO enhances a vehicle's ability to avoid collisions when operating in uncertain environments.
An Efficient Cloud Forensic Approach for IaaS, SaaS and PaaS Model

The securing of sophisticated artifacts by computerized forensics is a crucial phase in the forensic process. There is no such system for protecting and analyzing software systems in support of daily forensic knowledge, as illustrated in previous studies. When a customer or law enforcement department needs evidence, it is essential to come back to CSPs to show that and collect that irrespective of the condition the details might be in. Cloud data actually exists in a computer case, and depending on the CSP, is inevitable. This poses similar issues in itself, inquiry assumes partnership, pause response, unreasonable evidence and possible single intent of failure. While prior inquiries have recommended an IaaS framework, this relies on the compilation and availability of VM files at a rather simple level, and a key point of view of the approach is to incorporate the CSP as a core part of the structure in general and ensure additional data is obtained from cloud management. Nevertheless, the problem of reliance on the CSP and its associated matters is not addressed in this way. This paper briefly introduces a innovative solution that integrates accessing the virtual hard disk from the cloud environment at any given time with no requirement to use the CSP or some alteration to the basic basis of the CSP. Such an method will be tested experimentally in order to see its achievability. In this article, three experiments are performed to investigate the tolerability, efficiency of the solution and to provide a deeper understanding of both the technical recommendations resulting from such a method about the day-to-day function of a cloud framework and the costs.
Exploiting DRAM bank mapping and HugePages for effective denial-of-service attacks on shared cache in multicore

In this paper, we propose memory-aware cache DoS attacks that can induce more effective cache blocking by taking advantage of information of the underlying memory hardware. Like prior cache DoS attacks, our new attacks also generate lots of cache misses to exhaust cache internal shared hardware resources. The difference is that we carefully control those cache misses to target the same DRAM bank to induce bank conflicts. Note that accesses to different DRAM banks can occur in parallel, and are thus faster. However, accesses to the same bank are serialized, and thus slower [5] and as each memory access request takes longer to finish, it would prolong the time it takes for the cache to become unblocked. We further extend these attacks to exploit HugePage support in Linux in order to directly control physical address bits and to avoid TLB contention, while mounting the attacks from the userspace.
Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning

Radial basis function (RBF) networks are widely adopted to solve problems in the field of pattern classification. However, in the construction phase of such networks, there are several issues encountered, such as the determination of the number of nodes in the hidden layer, the form and initialization of the basis functions, and the learning of the parameters involved in the networks. In this paper, we present a novel approach for constructing RBF networks for pattern classification problems. An iterative self-constructing clustering algorithm is used to produce a desired number of clusters from the training data. Accordingly, the number of nodes in the hidden layer is determined. Basis functions are then formed, and their centers and deviations are initialized to be the centers and deviations of the corresponding clusters. Then, the parameters of the network are refined with a hybrid learning strategy, involving hyperbolic tangent sigmoid functions, steepest descent backpropagation, and least squares method. As a result, optimized RBF networks are obtained. With this approach, the number of nodes in the hidden layer is determined and basis functions are derived automatically, and higher classification rates can be achieved. Furthermore, the approach is applicable to construct RBF networks for solving both single-label and multi-label pattern classification problems.
Development of Intelligent Agricultural Machinery Control Device

Abstract This article is dedicated to creating a low-cost intelligent agricultural machinery control equipment based on ATmega 328p chip. The created control device is used to directly connect various peripheral sensors and actuators of the intelligent agricultural machinery to the computer. The controller circuit design diagram is given, and the control method is presented. The accuracy of the signal input/output channels of the control device to transmit signals was verified through experiments, and the control device was applied to the intelligent agricultural machinery experiment platform. The experimental results are analyzed and suggestions for further development of the control device are provided.
A New Reversible Database Watermarking Approach with Ant Colony Optimization Algorithm

Abstract In many fields such as medicine, science, transportation, it is often necessary to maintain the real-time information, and the data in the centralized database that saves data in these fields is distributed to various clients. On the insecure public Internet, there are distributed databases stored in data centers. Sharing may create risks, such as unauthorized copies, changes to data, and distribution of data to unauthorized persons for reuse. Many database watermarking methods have been proposed to protect digital rights. In order to reduce watermark distortion and increase watermark capacity, people pay more and more attention to the optimization of watermark technology. This paper proposes a method of embedding differential expansion watermarking (DEW) based on ant colony algorithm (ACO) in relational database. This method uses a secure hash algorithm for grouping, increasing the privacy of watermark embedding. The ant colony algorithm performs the optimization operation, finds the best attribute columns for embedding the watermark in the subgroup, and finally uses the differential expansion technology to embed the watermark. Experimental results show that compared with similar works reported in the literature, the reversible watermarking method based on ant colony algorithm database proposed in this paper not only increases the watermarking capacity, but also improves the robustness against various types of attacks.
Benchmarking a distributed database design that supports patient cohort identification

In this article we present the implementation and benchmarking of a medical information system on top of a distributed relational database system. We enhanced a distributed database system with the implementation of a clustering (based on similarity of disease terms) that induces a primary horizontal fragmentation of a data table and derived fragmentations of secondary tables. With our clustering-based fragmentation, data locality for similarity-based query answering is ensured so that data do not have to be sent unnecessarily over the network. In our benchmark we show that we achieve a significant efficiency gain when retrieving all relevant related answers.
What is the Value of Data? on Mathematical Methods for Data Quality Estimation

Data is one of the most important assets of the information age, and its societal impact is undisputed. Yet, rigorous methods of assessing the quality of data are lacking. In this paper, we propose a formal definition for the quality of a given dataset. We assess a dataset's quality by a quantity we call the expected diameter, which measures the expected disagreement between two randomly chosen hypotheses that explain it, and has recently found applications in active learning. We focus on Boolean hyperplanes, and utilize a collection of Fourier analytic, algebraic, and probabilistic methods to come up with theoretical guarantees and practical solutions for the computation of the expected diameter. We also study the behaviour of the expected diameter on algebraically structured datasets, conduct experiments that validate this notion of quality, and demonstrate the feasibility of our techniques.
Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs

Annual recruitment data of new graduates are manually analyzed by human resources (HR) specialists in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Different job seekers send applications to companies every year. The relationships between applicants’ attributes (e.g., English skill or academic credentials) can be used to analyze the changes in recruitment trends across multiple years. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship among applicant qualifications across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, HR specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets, and we discuss feedback from HR specialists obtained throughout Panacea’s development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates.
Reproducibility of two calibration procedures for phase-measuring deflectometry

Phase-measuring deflectometry is an optical inspection technique for reflective surfaces. It enables absolute, quantitative surface measurements, given a calibrated measurement setup. Two general calibration approaches can be found in literature: First, the stepwise approach uses a calibration pattern and determines internal camera parameters and external geometrical parameters in separate, consecutive steps. Second, the holistic approach optimizes all parameters collectively, based on deflectometric measurements of a calibration mirror. Whereas both approaches have been compared regarding the accuracy of subsequent surface measurements, the present contribution focuses on experimental examination of their reproducibility. In experiment E1, we assess the parameter variability by repeating both calibration procedures ten times. In an additional experiment E2, we repeat all calibration measurements related to a mirror/pattern position ten times in a row before rearranging the mirror/pattern, in order to examine the purely noise-related parameter variability. Finally, we calculate the coordinate variability of a set of world points projected onto the image planes of the calibrated cameras. The measured variability is consistently higher in E1 than in E2 (average ratio: 3.2). Unexpectedly, in both experiments, the external parameter variability also turns out to be higher for the holistic approach compared to stepwise calibration (average ratio: 2.3). This is of importance, since the holistic approach is known from literature to be more accurate than the stepwise approach, regarding their respective application to surface measurements. The image coordinate variability is comparable for both calibration approaches with an average of 0.84 and 0.21 camera pixels for E1 and E2, respectively.
A New Outlier Detection Method Based on Machine Learning

Aiming at the practical problems of outlier detection, such as unreasonable assumptions, uncertain thresholds and repeated manual debugging, a novel method of outlier detection based on machine learning is proposed. Firstly, the outlier factor of each data object is calculated by unsupervised learning. In this paper, Isolation Forest Algorithm is adopted to calculate the outlier factor. Then, the outlier detection model is established. The problem of outlier detection is transformed into a binary classification learning model. The outlier factor threshold is learned from labeled dataset by supervised learning, and the threshold is generated by the way of gradually progressive grid searching. The proposed method with other methods is further fused by ensemble learning, which improves the detection accuracy of outlier. We conduct extensive experiments on several datasets, and show that our new outlier detection method competes with Z-score method. Our method achieves a better performance in terms of accuracy and effectiveness.
Mismorphism: The Heart of the Weird Machine

Mismorphisms—instances where predicates take on different truth values across different interpretations of reality (notably, different actors’ perceptions of reality and the actual reality)—are the source of weird instructions. These weird instructions are tiny code snippets or gadgets that present the exploit programmer with unintended computational capabilities. Collectively, they constitute the weird machine upon which the exploit program runs. That is, a protocol or parser vulnerability is evidence of a weird machine, which, in turn, is evidence of an underlying mismorphism. This paper seeks to address vulnerabilities at the mismorphism layer. The work presented here connects to our prior work in language-theoretic security (LangSec). LangSec provides a methodology for eliminating weird machines: By limiting the expressiveness of the input language, separating and constraining the parser code from the execution code, and ensuring only valid input makes its way to the execution code, entire classes of vulnerabilities can be avoided. Here, we go a layer deeper with our investigation of the mismorphisms responsible for weird machines. In this paper, we re-introduce LangSec and mismorphisms, and we develop a logical representation of mismorphisms that complements our previous semiotic-triad-based representation. Additionally, we develop a preliminary set of classes for expressing LangSec mismorphisms, and we use this mismorphism-based scheme to classify a corpus of LangSec vulnerabilities.
Active Learning for Classification with Abstention

We consider the problem of binary classification with the caveat that the learner can abstain from declaring a label incurring a cost λ ∈ [0,1/2] in the process. This is referred to as the problem of binary classification with a fixed-cost of abstention. For this problem, we propose an active learning strategy that constructs a non-uniform partition of the input space and focuses sampling in the regions near the decision boundaries. Our proposed algorithm can work in all the commonly used active learning query models, namely membership-query, pool-based and stream-based. We obtain an upper bound on the excess risk of our proposed algorithm under standard smoothness and margin assumptions and demonstrate its minimax near-optimality by deriving a matching (modulo poly-logarithmic factors) lower bound. The achieved minimax rates are always faster than the corresponding rates in the passive setting, and furthermore the improvement increases with larger values of the smoothness and margin parameters.
Parameter-Efficient Deep Neural Networks With Bilinear Projections

Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy. Given a proper deep learning framework, it is generally possible to increase the depth or layer width to achieve a higher level of accuracy. However, the huge number of model parameters imposes more computational and memory usage overhead and leads to the parameter redundancy. In this paper, we address the parameter redundancy problem in DNNs by replacing conventional full projections with bilinear projections. For a fully-connected layer with $D$ input nodes and $D$ output nodes, applying bilinear projection can reduce the model space complexity from $\mathcal{O}(D^2)$ to $\mathcal{O}(2D)$, achieving a deep model with a sub-linear layer size. However, structured projection has a lower freedom of degree compared to the full projection, causing the under-fitting problem. So we simply scale up the mapping size by increasing the number of output channels, which can keep and even boosts the model accuracy. This makes it very parameter-efficient and handy to deploy such deep models on mobile systems with memory limitations. Experiments on four benchmark datasets show that applying the proposed bilinear projection to deep neural networks can achieve even higher accuracies than conventional full DNNs, while significantly reduces the model size.
Injective Objects and Fibered Codensity Liftings

Functor lifting along a fibration is used for several different purposes in computer science. In the theory of coalgebras, it is used to define coinductive predicates, such as simulation preorder and bisimilarity. Codensity lifting is a scheme to obtain a functor lifting along a fibration. It generalizes a few previous lifting schemes including the Kantorovich lifting. In this paper, we seek a property of functor lifting called fiberedness. Hinted by a known result for Kantorovich lifting, we identify a sufficient condition for a codensity lifting to be fibered. We see that this condition applies to many examples that have been studied. As an application, we derive some results on bisimilarity-like notions.
Resolution Limits of Non-Adaptive Querying for Noisy 20 Questions Estimation

We study fundamental limits of estimation accuracy for the noisy 20 questions problem with measurement-dependent noise and introduce optimal non-adaptive procedures that achieve these limits. The minimal achievable resolution is defined as the absolute difference between the estimated and the true values of the target random variable, given a finite number of queries constrained by the excess-resolution probability. Inspired by the relationship between the 20 questions problem and the channel coding problem, we derive non-asymptotic bounds on the minimal achievable resolution. Furthermore, applying the Berry-Esseen theorem to our non-asymptotic bounds, we obtain a second-order asymptotic approximation to finite blocklength performance, specifically the achievable resolution of optimal non-adaptive query procedures with a finite number of queries subject to the excess-resolution probability constraint.
From Blockchain to Web

Blockchain systems make use of the Internet infrastructure to connect thousands of nodes in peer-to-peer (P2P) networks. These connected nodes form complex ecosystems encompassing various technologies that collaborate to offer millions of users distributed, decentralized, secure, and read-only data stores. With the proliferation of various private and public platforms and applications that are built on top of blockchain technology, rich sources of several types of simple or complex datasets have been created. Consequently, new requirements for managing these rich data sources, integrating them with their hosting environment, and making them discoverable have emerged. Fulfilling these requirements calls for extending the architecture of current blockchain systems by adding different layers that offer dedicated services for handling the data and allowing for integrating the blockchain-based platforms and applications with other systems, including Web-based applications. In this paper, we present a novel approach aiming at extending the architecture of current blockchain systems by adding new service layers for processing the data and offering new services to provide a bridge between blockchain systems and Web-based external applications enabling them to query, retrieve, and access the datasets managed by robust permission-based access control.