Reviewerly/exHarmony huggingface dataset:
Safe-by-Repair: A Convex Optimization Approach for Repairing Unsafe Two-Level Lattice Neural Network Controllers

In this paper, we consider the problem of repairing a data-trained Rectified Linear Unit (ReLU) Neural Network (NN) controller for a discrete-time, input-affine system. That is, we assume such a NN controller is given, and seek to repair unsafe closed-loop behavior at one known "counterexample" state, without violating closed-loop safety on a separate set of states. Our main result is an algorithm that can systematically and efficiently perform such repair, assuming that the controller has a Two-Level Lattice (TLL) architecture. In particular, we show sufficient conditions for the TLL repair problem can be formulated as two separate, but largely decoupled convex optimization problems: one of essentially local scope and one of essentially global scope. Furthermore, we use our algorithm to repair a TLL controller trained for a four-wheel-car dynamical model.
Fisher-Yates shuffle algorithm for randomization math exam on computer based-test

The aim of this study is to implement Fisher-Yates Shuffle Algorithm (FYSA) for randomization Math Exam in Computer Based-Test with fast execution time and can be generated without repetition and duplication. This study case in Management and Science University (MSU) Malaysia. This study used ADDIE model and develop based on a website framework using PHP as a back-end programming language, Java script as a client-side programming language, and MySQL as a database. The target population of this study was students in Bachelor Level at Management and Science University (MSU) Malaysia in Mathematics Subject. The subject will be focused on probability and statistics in one focus group. Mathematics lecturer also involved as expert in this study. There are seven questions set with a total questions N=15 on each set. The probability value of randomization the question set is 1/7 = 0.14285714. The permutation obtained from the randomization of Fisher Yates Shuffle is 15! = 1.307674368 x 1012. Based on the results of the quality test, it can be seen that all items of category regarding accessibility, helpful tools, and CBT content have an average percentage score 76.94%, these results mean that all CBT items are works well. That means the system will produce different random permutations each time students take question.
A Multi-task Approach to Neural Multi-label Hierarchical Patent Classification Using Transformers

With the aim of facilitating internal processes as well as search applications, patent offices categorize documents into taxonomies such as the Cooperative Patent Categorization. This task corresponds to a multi-label hierarchical text classification problem. Recent approaches based on pre-trained neural language models have shown promising performance by focusing on leaf-level label prediction. Prior works using intrinsically hierarchical algorithms, which learn a separate classifier for each node in the hierarchy, have also demonstrated their effectiveness despite being based on symbolic feature inventories. However, training one transformer-based classifier per node is computationally infeasible due to memory constraints. In this work, we propose a Transformer-based Multi-task Model (TMM) overcoming this limitation. Using a multi-task setup and sharing a single underlying language model, we train one classifier per node. To the best of our knowledge, our work constitutes the first approach to patent classification combining transformers and hierarchical algorithms. We outperform several non-neural and neural baselines on the WIPO-alpha dataset as well as on a new dataset of 70k patents, which we publish along with this work. Our analysis reveals that our approach achieves much higher recall while keeping precision high. Strong increases on macro-average scores demonstrate that our model also performs much better for infrequent labels. An extended version of the model with additional connections reflecting the label taxonomy results in a further increase of recall especially at the lower levels of the hierarchy.
Expression-Aware Face Reconstruction via a Dual-Stream Network

Recently, 3D face reconstruction from a single image has achieved promising progress by adopting the 3D Morphable Model (3DMM). However, face images taken in-the-wild usually involve expressions with a large range of variety. This poses difficulty to use 3DMM to represent such various facial expressions owing to the limited expressive ability of its linear model, thereby resulting in distortion and ambiguity in local facial regions. To tackle this problem, we present a novel dual-stream network composed of a geometry stream and a texture stream to deal with expression variations. Specifically, in the geometry stream, we propose novel Attribute Spatial Maps (ASMs) to decompose a face into the identity and expression attributes and then separately record the essential spatial information of the two facial attributes in the 2D image space. This avoids the interaction between the two attributes, thus preserving the identity information and further improving the ability of coping with expression variations. In the texture stream, we propose to generate facial appearance with realistic texture and canonical layout by our Semantic Region Stylization Mechanism (SRSM), that transfers the style from an input face to a 3DMM albedo map in a region-adaptive manner. Moreover, we also propose a Shared Semantic Region Prediction Module (SSRPM) to explore the common correspondence of semantic regions between the above two face texture representations. Both quantitative and qualitative evaluations on public datasets demonstrate the effectiveness of our approach in face reconstruction under expression variations.
Evaluation of statistical and Haralick texture features for lymphoma histological images classification

The investigation of different types of cancer can be performed by images classification with features extracted from specific regions identified by a segmentation step. Therefore, this study presents the evaluation of texture features extracted from neoplastic nuclei for the classification of lymphomas images. The neoplastic nuclei were segmented by steps of pre and post-processing and a thresholding. Statistical and Haralick's features extracted from wavelet and ranklet transforms were evaluated with different classifiers. The use of the statistical metrics from the wavelet transform in association with the K-nearest neighbour classifier provided the best results in most of the two-class classifications.
Guidance Point Generation-Based Cooperative UGV Teleoperation in Unstructured Environment

Teleoperation is widely used for unmanned ground vehicle (UGV) navigation in military and civilian fields. However, the human operator has to limit speed to ensure the handling stability because of the low resolution of video, limited field of view and time delay in the control loop. In this paper, we propose a novel guidance point generation method that is well suited for human–machine cooperative UGV teleoperation in unstructured environments without a predefined goal position. The key novelty of this method is that the guidance points used for navigation can be generated with only the local perception information of the UGV. Firstly, the locally occupied grid map (OGM) was generated utilizing a probabilistic grid state description method, and converted into binary image to constructed the convex hull of obstacle area. Secondly, we proposed an improved thinning algorithm to extract skeletons of navigable regions from binary images, and find out the target skeleton related to the position of the UGV utilizing the k-nearest neighbor (kNN) algorithm. The target skeleton was reconstructed at the midline position of the navigable region using the decreasing gradient algorithm in order to obtain the appropriate skeleton end points for use as candidate guidance points. For visually presenting the driving trend of the UGV and convenient touch screen operation, we transformed guidance point selection into trajectory selection by generating the predicted trajectory correlative to candidate guidance points based on the differential equation of motion. Experimental results show that the proposed method significantly increases the speed of teleoperated UGV.
Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities

The KNN algorithm is one of the most famous algorithms in machine learning and data mining. It does not preprocess the data before classification, which leads to longer time and more errors. To solve the problems, this paper first proposes a PK-means++ algorithm, which can better ensure the stability of a random experiment. Then, based on it and spherical region division, an improved KNNPK+ is proposed. The algorithm can select the center of the spherical region appropriately and then construct an initial classifier for the training set to improve the accuracy and time of classification.
Off-chain state channels in the energy domain

Blockchain technology has attracted attention in the energy domain as a new decentralized infrastructure, with startups and researchers presenting new solutions to bring the benefits of decentralization to energy-related use cases. However, blockchains, or more specifically, public permissionless blockchains do not scale well. Scalability of blockchain solutions is often left as future work. Off-chain protocols such as payment channels, or in general, state channels are ways to improve the scalability of blockchain-based applications. The benefits of these state-of-the-art state channels in scaling blockchain-based applications in the energy domains have still not been investigated. Thus, we present a methodology to indicate if the various use cases in the energy domain benefit from state channels. Furthermore, we systematically assess these use cases by applying our proposed methodology. We found that state channels improve the scalability of various energy-related use cases, such as energy trading, and help in solving energy optimization problems.
Risk-sensitive optimization for robust quantum controls

Highly accurate and robust control of quantum operations is vital for the realization of error-correctible quantum computation. In this paper, we show that the robustness of high-precision controls can be remarkably enhanced through sampling-based stochastic optimization of a risk-sensitive loss function. Following the stochastic gradient-descent direction of this loss function, the optimization is guided to penalize poor-performance uncertainty samples in a tunable manner. We propose two algorithms, which are termed as the risk-sensitive GRAPE and the adaptive risk-sensitive GRAPE. Their effectiveness is demonstrated by numerical simulations, which is shown to be able to achieve high control robustness while maintaining high fidelity.
Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil

Phrase-based statistical machine translation (PB-SMT) has been the dominant paradigm in machine translation (MT) research for more than two decades. Deep neural MT models have been producing state-of-the-art performance across many translation tasks for four to five years. To put it another way, neural MT (NMT) took the place of PB-SMT a few years back and currently represents the state-of-the-art in MT research. Translation to or from under-resourced languages has been historically seen as a challenging task. Despite producing state-of-the-art results in many translation tasks, NMT still poses many problems such as performing poorly for many low-resource language pairs mainly because of its learning task’s data-demanding nature. MT researchers have been trying to address this problem via various techniques, e.g., exploiting source- and/or target-side monolingual data for training, augmenting bilingual training data, and transfer learning. Despite some success, none of the present-day benchmarks have entirely overcome the problem of translation in low-resource scenarios for many languages. In this work, we investigate the performance of PB-SMT and NMT on two rarely tested under-resourced language pairs, English-To-Tamil and Hindi-To-Tamil, taking a specialised data domain into consideration. This paper demonstrates our findings and presents results showing the rankings of our MT systems produced via a social media-based human evaluation scheme.
Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance

We construct more efficient cryptosystems with provable security against adaptive attacks, based on simple and natural hardness assumptions in the standard model. Concretely, we describe: An adaptively-secure variant of the efficient, selectively-secure LWE-based identity-based encryption (IBE) scheme of Agrawal, Boneh, and Boyen (EUROCRYPT 2010). In comparison to the previously most efficient such scheme by Yamada (CRYPTO 2017) we achieve smaller lattice parameters and shorter public keys of size \(\mathcal {O} (\log \lambda )\), where \(\lambda \) is the security parameter. Adaptively-secure variants of two efficient selectively-secure pairing-based IBEs of Boneh and Boyen (EUROCRYPT 2004). One is based on the DBDH assumption, has the same ciphertext size as the corresponding BB04 scheme, and achieves full adaptive security with public parameters of size only \(\mathcal {O} (\log \lambda )\). The other is based on a \(q \)-type assumption and has public key size \(\mathcal {O} (\lambda )\), but a ciphertext is only a single group element and the security reduction is quadratically tighter than the corresponding scheme by Jager and Kurek (ASIACRYPT 2018). A very efficient adaptively-secure verifiable random function where proofs, public keys, and secret keys have size \(\mathcal {O} (\log \lambda )\). As a technical contribution we introduce blockwise partitioning, which leverages the assumption that a cryptographic hash function is weak near-collision resistant to prove full adaptive security of cryptosystems.
Breast Cancer Lesion Detection and Classification in mammograms using Deep Neural

Abstract A method to automatically detect and classify a lesion into either malignant or non-malignant is presented in this work. The dataset used is obtained from INbreast database and are in format of full-field digital mammography (FFDM). Some of the key challenges in detecting cancerous lesion in mammography are the low contrast between cancerous lesion and its surrounding tissues, apparent contrast similarities between lesion and pectoral muscle, presence of calcifications that may disrupt the detection process, and some level of morphological similarities between the lesion and some normal tissues. The work here consists of two main parts. The first part is the image processing section that aims to sample the lesion with intended lesion-to-surrounding ratio (0.4-0.6) and to avoid sampling from unintended regions such as pectoral muscle. Another key challenge is that the database is relatively small while machine learning requires a relatively large dataset. To improve size of samples, eighty fixed-size images (250 pixels x 250 pixels) are randomly cropped out of each of the previously processed image. The second part is to build the machine learning application based on deep neural network framework to classify samples into two classes, malignant and non-malignant. In present work we apply two different frameworks known as Plain and Residual Net (ResNet). Our calculations show that both models can detect a single lesion with more than 90% accuracy and area under ROC curve &gt;0.94.
A binary decision diagram lifted domain for analyzing program families

Many software systems today are highly configurable. They can produce a potentially large variety of related programs (variants) by selecting suitable configuration options (features) at compile time. Recently, specialized variability-aware (lifted, family-based) static analyses based on abstract interpretation have been developed. They allow analyzing all variants of a program family (or, any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. In effect, they produce precise analysis results for all individual variants. The elements of the underlying lifted analysis domain represent tuples (i.e. disjunction of properties), which maintain one property from an existing single-program analysis domain per variant. Nevertheless, explicit property enumeration in tuples, one by one for all variants, immediately yields to combinatorial explosion given that the number of variants can grow exponentially with the number of features. Therefore, such lifted analyses may be too costly or even infeasible for program families with a large number of variants. In this work, we propose a more efficient lifted static analysis of program families with Boolean features, where sharing is explicitly possible between analysis elements corresponding to different variants. This is achieved by giving a symbolic representation of the lifted analysis domain, which can efficiently handle disjunctive properties in program families. The elements of the new lifted domain are binary decision diagrams, where decision nodes are labeled with Boolean features and leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the abstract (property) domain for leaf nodes. To illustrate the potential of this representation, we have implemented a lifted static analyzer that uses a combination of forward and backward analyses for inferring numerical invariants and necessary preconditions of C program families. It uses APRON and BDDAPRON libraries for implementing the new lifted analysis domain. The APRON library, used for the leaves, is a widely accepted API for numerical abstract domains (e.g. polyhedra, octagons, intervals), while the BDDAPRON is an extension of APRON which adds the power domain of Boolean formulae and any APRON domain. An empirical evaluation on C benchmarks taken from SV-COMP and BusyBox indicates that our binary decision diagram-based approach is effective and outperforms the baseline tuple-based approach.
A Fusion Algorithm for Solving the Hidden Shift Problem in Finite Abelian Groups

It follows from a result by Friedl, Ivanyos, Magniez, Santha and Sen from 2014 that, for any fixed integer $$m > 0$$ (thought of as being small), there exists a quantum algorithm for solving the hidden shift problem in an arbitrary finite abelian group $$(G, +)$$ with time complexity $$\begin{aligned} \mathrm {poly}( \log |G|) \cdot 2^{O(\sqrt{\log |mG|})}. \end{aligned}$$ As discussed in the current paper, this can be viewed as a modest statement of Pohlig–Hellman type for hard homogeneous spaces. Our main contribution is a somewhat simpler algorithm achieving the same runtime for $$m = 2^tp$$ , with t any non-negative integer and p any prime number, where additionally the memory requirements are mostly in terms of quantum random access classical memory; indeed, the amount of qubits that need to be stored is $$\mathrm {poly}( \log |G|)$$ . Our central tool is an extension of Peikert's adaptation of Kuperberg's collimation sieve to arbitrary finite abelian groups. This allows for a reduction, in said time, to the hidden shift problem in the quotient $$G/2^tpG$$ , which can then be tackled in polynomial time, by combining methods by Friedl et al. for p-torsion groups and by Bonnetain and Naya-Plasencia for $$2^t$$ -torsion groups.
Investigating 4D movie audiences’ emotional responses to motion effects and empathy

Designing 4D effects corresponding to audiences' emotional responses is important because 4D effects are critical components of 4D movies that provide rich emotional experiences. The recent increasing popularity of 4D content has led to the development of motion effect technology, which involves motions of the chair according to the scene. However, it is difficult to find studies that systematically investigated the influence of motion effects on audiences' emotional responses. This study investigated the emotional responses of a 4D movie audience to motion effects according to their level of empathy. Participants (mean: 25.0 years, standard deviation: 5.0) with varying levels of empathy watched movie clips provided with or without single pitch motion effects. The degree that the motion effect and empathy affected the elicited emotions differed depending on the emotion type. For example, participants with high empathy reported stronger intensity of fear when short and weak motion effects were exhibited than when there was no motion effect. Distinct motion effect design guidelines that can be adopted to enhance audiences’ emotional experiences were proposed. The findings can be referred to when investigating the emotional responses of 4D movie audiences.
Negation as a modality in a quantified setting

Abstract The idea of treating negation as a modality manifests itself in various logical systems, especially in Došen’s propositional logic $\textsf {N}$, whose negation is weaker than that of Johansson’s minimal logic. Among the interesting extensions of $\textsf {N}$ are the propositional logics $\textsf {N}^{\ast }$ and $\textsf {Hype}$; the former was proposed in Cabalar et al. (2006, Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning, 25–36), while the latter has recently been advocated in Leitgeb (2019, J. Philos. Logic, 48, 305–405), but was first introduced in Moisil (1942, Disquisitiones Math. et Phys., 2, 3–98). I shall develop predicate versions of $\textsf {N}$ and $\textsf {N}^{\ast }$ and provide a simple Routley-style semantics for the predicate version of $\textsf {Hype}$. The corresponding strong completeness results will be proved by means of a useful general technique. It should be remarked that this work can also be seen as a starting point for the investigation of intuitionistic predicate modal logics.
NOAH

Spreadsheet systems are by far the most popular platform for data exploration on the planet, supporting millions of rows of data. However, exploring spreadsheets that are this large via operations such as scrolling or issuing formulae can be overwhelming and error-prone. Users easily lose context and suffer from cognitive and mechanical burdens while issuing formulae on data spanning multiple screens. To address these challenges, we introduce dynamic hierarchical overviews that are embedded alongside spreadsheets. Users can employ this overview to explore the data at various granularities, zooming in and out of the spreadsheet. They can issue formulae over data subsets without cumbersome scrolling or range selection, enabling users to gain a high or low-level perspective of the spreadsheet. An implementation of our dynamic hierarchical overview, NOAH, integrated within DataSpread, preserves spreadsheet semantics and look and feel, while introducing such enhancements. Our user studies demonstrate that NOAH makes it more intuitive, easier, and faster to navigate spreadsheet data compared to traditional spreadsheets like Microsoft Excel and spreadsheet plug-ins like Pivot Table, for a variety of exploration tasks; participants made fewer mistakes in NOAH while being faster in completing the tasks.