Reviewerly/exHarmony huggingface dataset:
An Expressiveness Hierarchy of Behavior Trees and Related Architectures

In this letter, we provide a formal framework for comparing the expressive power of Behavior Trees (BTs) to other action selection architectures. Taking inspiration from the analogous comparisons of structural programming methodologies, we formalise the concept of `expressiveness'. This leads us to an expressiveness hierarchy of control architectures, which includes BTs, Decision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines (FSMs). By distinguishing between BTs with auxiliary variables and those without, we demonstrate the existence of a trade-off in BT design between readability and expressiveness. We discuss what this means for BTs in practice.
ConNet: Deep Semi-Supervised Anomaly Detection Based on Sparse Positive Samples

Existing semi-supervised anomaly detection methods usually use a large amount of labeled normal data for training, which have the problem of high labeling costs.Only a few semi-supervised methods utilize unlabeled data and a few labeled anomalies to train models.However, these kinds of methods usually encounter two problems: (i) since anomalies usually have different behavior patterns or the internal mechanisms that produce anomalies are complex and diverse, a few labeled anomalies cannot cover all anomaly types; and (ii) the amount of unlabeled data in the training set is substantially greater than the amount of labeled data, which leads to that unlabeled data with contamination often dominates the training process.To solve these two problems, we propose the semi-supervised anomaly detection method named ConNet and a new loss function named concentration loss.Specifically, ConNet consists of two stages.Firstly, we obtain the prior anomaly score of unlabeled data via prior estimation module and attach the prior anomaly score to unlabeled data as the training weight.Then, an anomaly scoring network is training to assign anomaly scores to data instances, which can ensure that the anomaly scores of anomalies significantly deviate from those of normal instances.We have conducted experiments on thirteen real-world data sets and tested the performance of our method in terms of detection accuracy, utilization efficiency of labeled data, and robustness to different contamination rates.The experimental results show that the performance of our method is significantly better than those of the state-of-the-art anomaly detection methods.
Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling

Unsupervised document classification for imbalanced data sets poses a major challenge. To obtain accurate classification results, training data sets are often created manually by humans which requires expert knowledge, time and money. Depending on the imbalance of the data set, this approach also either requires human labelling of all of the data or it fails to adequately recognize underrepresented categories. We propose an integration of web scraping, one-class Support Vector Machines (SVM) and Latent Dirichlet Allocation (LDA) topic modelling as a multi-step classification rule that circumvents manual labelling. Unsupervised one-class document classification with the integration of out-of-domain training data is achieved and >80% of the target data is correctly classified. The proposed method thus even outperforms common machine learning classifiers and is validated on multiple data sets.
The interplay of a conversational ontology and AI planning for health dialogue management

Health dialogue systems are required to respect some special requirements such as predictability and reliability. While knowledge based approaches still seem to be the most appropriate for these systems, the automated generation of reliable policies remains an open problem. This work proposes an approach that integrates a conversational ontology (Convology) and Artificial Intelligence planning with the aim of automating the generation of a dialogue manager capable of handling goal-oriented dialogues for the health domain. The resulting dialogue manager is aimed to be integrated into a suitable architecture that provides the natural language components. We illustrate our approach by describing how it has been implemented into PuffBot, a multi-turn goal-oriented conversational agent for supporting patients affected by asthma.
Performance comparison of feature selection and extraction methods with random instance selection

In pattern recognition, irrelevant and redundant features together with a large number of noisy instances in the underlying dataset decrease performance of trained models and make the training process considerably slower, if not practically infeasible. In order to combat this so-called curse of dimensionality, one option is to resort to feature selection (FS) methods designed to select the features that contribute the most to the performance of the model, and one other option is to utilize feature extraction (FE) methods that map the original feature space into a new space with lower dimensionality. These two methods together are called feature reduction (FR) methods. On the other hand, deploying an FR method on a dataset with massive number of instances can become a major challenge, from both memory and run time perspectives, due to the complex numerical computations involved in the process. The research question we consider in this study is rather a simple, yet novel one: do these FR methods really need the whole set of instances (WSI) available for the best performance, or can we achieve similar performance levels with selecting a much smaller random subset of WSI prior to deploying an FR method? In this work, we provide empirical evidence based on comprehensive computational experiments that the answer to this critical research question is in the affirmative. Specifically, with simple random instance selection followed by FR, the amount of data needed for training a classifier can be drastically reduced with minimal impact on classification performance. We also provide recommendations on which FS/ FE method to use in conjunction with which classifier.
From WASD to BLS with application to pattern classification

The single-hidden-layer feedforward neural network (SLFN) exhibits an excellent approximation ability, despite its simple structure. This study examines two neural network models developed based on the general structure of the SLFN, i.e., the weights-and-structure-determination (WASD) neural network and broad learning system (BLS). The BLS and WASD neural networks exhibit many similarities in terms of their structure and algorithm, while BLS incorporates certain distinct ideas that render it an improved version of the WASD neural network. The research described in this paper is the first approach to establish a connection between the BLS and WASD neural networks. Moreover, pattern classification experiments on a foot dataset and several benchmark datasets are conducted using these two kinds of neural network models, and the performance of different models is compared and analyzed. Experimental results show that dividing the feature nodes into one group is the best choice for our foot dataset, and that the cross-layer connectivity and sparse coding of BLS can effectively improve the classification accuracy. In addition, it is shown that the dynamic update algorithm of BLS can improve the accuracy and save a lot of time when updating the model structure, but the final accuracy depends on the initial structure and parameters. Comparative experiments show that BLS achieves 89.12% classification accuracy for the foot dataset, which is the highest among all the demonstrated WASD neural networks and two other SLFN models. Finally, extended experiments on five benchmark datasets show that BLS can achieve satisfactory accuracy on multiple types of datasets, while WASD neural networks are possibly inadequate in processing image datasets in contrast. • It is the first attempt to establish a relation between BLS and WASD neural networks. • A novel structure for the BLS equivalent to the original structure is established. • Aspects that make BLS outperform WASD neural networks are clarified and demonstrated. • The effects of BLS and multiple WASD neural networks in real scenarios are compared.
Drivers and barriers of virtual reality adoption in UK AEC industry

Purpose Virtual reality (VR) offers unique features of a three-dimensional (3D) model during early design stages in the virtual environment with immersive functions. Although the potential of VR is to increase the effectiveness and productivity of the project phases from initial concept design to detailed design preparation. VR adoption in the United Kingdom (UK) Architectural Engineering and Construction (AEC) sector is slow compared to other sectors. This research focuses on ascertaining the drivers and barriers of VR in construction projects in the UK. Design/methodology/approach The study adopts an online survey design. It uses Bristol Online Survey (BOS) to create a structured questionnaire that is used to assess UK construction professionals using a convenience sampling technique. Therefore, researcher uses descriptive and inferential technique for data analysis and presentation based on Statistical Package for Social Sciences (SPSS) to analyze the questionnaire. Findings The research findings revealed the most significant barriers to VR adoption in UK construction industry were lack of skills/expertise and cultural change. Hence, the main drivers of VR adoption as rated by the professionals in the UK construction industry are improved safety, improved quality and improved productivity. Practical implications The identification and assessment of the drivers and barriers to VR adoption could advance VR adoption among construction professionals and other stakeholders of the UK AEC sector. This could also be extended to developing countries, given the status of VR as being in the developing stage. Originality/value This study provides valuable insights to construction professionals and stakeholders to plan actions that could enhance the drivers and mitigate the barriers of VR. This study's main contribution is to group and classify various drivers and barriers into easily understood categories, in order to potentiate the drivers and reduce the barriers effectively. The groupings could be used as benchmarks in similar studies in developing countries.
On the proliferation of support vectors in high dimensions*

Abstract The support vector machine (SVM) is a well-established classification method whose name refers to the particular training examples, called support vectors, that determine the maximum margin separating hyperplane. The SVM classifier is known to enjoy good generalization properties when the number of support vectors is small compared to the number of training examples. However, recent research has shown that in sufficiently high-dimensional linear classification problems, the SVM can generalize well despite a proliferation of support vectors where all training examples are support vectors. In this paper, we identify new deterministic equivalences for this phenomenon of support vector proliferation, and use them to (1) substantially broaden the conditions under which the phenomenon occurs in high-dimensional settings, and (2) prove a nearly matching converse result.
Common Counters: Compressed Encryption Counters for Secure GPU Memory

Hardware-based trusted execution has opened a promising new opportunity for enabling secure cloud computing. Nevertheless, the current trusted execution environments are limited to the traditional CPU-based computation, while more and more critical workloads such as machine learning require to use GPUs. For secure GPU computing, recent studies proposed to isolate critical GPU operations from the operating system by providing trusted execution environments for GPUs. However, a missing component in the prior GPU trusted execution studies is a hardware-based memory protection technique optimized for GPUs. Start-of-art memory protection techniques for CPUs use counter-mode encryption, where per-block counters are used as timestamps for generating one-time pads. The integrity of counters is protected by a counter tree. This study investigates the hardware memory encryption for GPUs and identifies counter cache misses as one of the key performance bottlenecks. To mitigate the overheads of counter cache misses, this paper proposes a novel technique, called common counters, for efficient counter-mode encryption and counter integrity protection. The proposed technique exploits unique characteristics of common GPU applications. In GPU applications, a large portion of application memory is written only once by the initial data transfer from the CPU memory, or the number of writes by the GPU applications to major data structures tends to be uniform. Exploiting the uniform write property, this study proposes to use a common counter representation to complement the existing per-block encryption counters. With the common counters, the proposed technique can almost eliminate the performance overheads caused by counter cache misses, reducing the degradation to 2.9%.
Synthesizing ReLU neural networks with two hidden layers as barrier certificates for hybrid systems

Barrier certificates provide safety guarantees for hybrid systems. In this paper, we propose a novel approach to synthesizing neural networks as barrier certificates. Candidate networks are trained from a special structure: ReLU neural networks consisting of two hidden layers. Then, the problem of identifying real barrier certificates from candidates is transformed into a group of mixed integer linear programming problems and a mixed integer quadratically constrained problem. Taking full advantage of the recent advance in optimization, barrier certificates validation can be performed effectively. We implement the tool SyntheBC and evaluate its performance over 3 hybrid systems and 8 continuous systems up to 12-dimensional state space. The experimental results show that our method is more scalable and effective than the classical polynomial barrier certificate method and the existing neural network based method.
Advanced Encryption Standard: Attacks and Current Research Trends

The Advanced Encryption Standard (AES), also known as a specification for the encryption of electronic data. AES is comparatively highly secure and more efficient than well-known other cryptographic algorithms and existing Data Encryption Standard (DES). This work is focused in analyzing selecting valuable research publications on AES. This also discusses some machine learning techniques such as artificial neural network (ANN) used to protect side channel attack for images. Besides this, many other recent developments such as effective differential power analysis (DPA) technique and hybrid encryption algorithm using LZW have been discussed and analyzed in details. Finally, the paper concludes with a systematic analysis of different types of attacks on AES and some future research aspects in this context. Finally, this work also proposes a new model for attack detection in AES.
TILT: Achieving Higher Fidelity on a Trapped-Ion Linear-Tape Quantum Computing Architecture

Trapped-ion qubits are a leading technology for practical quantum computing. In this work, we present an architectural analysis of a linear-tape architecture for trapped ions. In order to realize our study, we develop and evaluate mapping and scheduling algorithms for this architecture. In particular, we introduce TILT, a linear “Turing-machinelike” architecture with a multilaser control “head,” where a linear chain of ions moves back and forth under the laser head. We find that TILT can substantially reduce communication as compared with comparable-sized Quantum Charge Coupled Device (QCCD) architectures. We also develop two important scheduling heuristics for TILT. The first heuristic reduces the number of swap operations by matching data traveling in opposite directions into an “opposing swap.”, and also avoids the maximum swap distance across the width of the head, as maximum swap distances make scheduling multiple swaps in one head position difficult. The second heuristic minimizes ion chain motion by scheduling the tape to the position with the maximal executable operations for every movement. We provide application performance results from our simulation, which suggest that TILT can outperform QCCD in a range of NISQ applications in terms of success rate (up to 4.35x and 1.95x on average). We also discuss using TILT as a building block to extend existing scalable trapped-ion quantum computing proposals.
Application of Multimedia Networks in Business English Teaching in Vocational College

To better meet the needs of economic restructuring and industrial development in China, educators in vocational colleges have to find new approaches to narrow the gaps between the updated requirements from the employers for business English majors and the vocational college education reality. Under this circumstance, a tentative study was carried out with the aids of multimedia to help the educators remove obstacles blocking achieving their educational aim, which is cultivating the graduates who are qualified for the social requirement. This study mainly discussed how effective the multimedia networks can help the business English majors in vocational colleges to enrich their work experience and enhance their practical English ability, computer skills, communication skills, and cooperation skills comprehensively. It aimed at exploring the ways of helping the business English majors in vocational colleges to gain work experience and improve their practical English skills, computer skills, communication skills, and cooperation skills with the aid of multimedia networks. An experimental teaching program was designed under the guidelines of a task-based teaching approach and constructive learning theory. The program includes four teaching models, which are in-class learning and field preparation, mock practice, in-field learning and field education, and learning reflection. All learning and field practices are delivered online facilitated by multimedia networks. One hundred and forty sophomores majoring in business English are selected as participants of the program. Data of students' confidence in all five monitored essential abilities are collected before and after the learning procedure through questionnaires, and students' feedbacks are collected through interviews. The statistical results show that multimedia networks can help students improve their overall competence, especially in gaining work experience and practical English skills. Moreover, adopting multimedia networks in teaching can provide more internship opportunities, enrich teaching scenarios, and prolong net teaching hours, which forms a preferred learning environment and provides better learning outcomes. It could also be used to assist communication and cooperation trainings.
Machine learning applied to diabetes dataset using Quantum versus Classical computation

This paper presents a Quantum versus classical implemented of Machine learning (ML) algorithm applied to a diabetes dataset. Diabetes is a Sixth deadliest disease in the world and approximately 10 million new cases are registered every year worldwide. Using novel Quantum computing (QC) along with Quantum Machine Learning (QML) techniques in the healthcare system to improve and accelerate the computing of existing ML models that allows the different approach to understanding the complex patterns of the disease. The proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases. Our study compares classical and quantum algorithms, namely Decision Tree, Random Forest, Extreme Boosting Gradient and Adaboost, Qboost, Voting Model 1, Voting Model 2, Qboost Plus, New model 1 and New Model 2 along with an ensemble method which creates a strong classifier from a committee of weak classifiers. The results we achieved using the validation metrics of the New Model 1 showed an overall precision of 69%, a recall of 69%, an F1-Score of 69%, a specificity of 69% and an accuracy of 69% on our diabetes dataset, with an increase of the computation speed by 55 times in comparison of the classical system. Our study has proved that QC improves the computational speed and its inclusion in medical applications will deliver faster results to physicians and caregivers.
Multiparty Reusable Non-interactive Secure Computation from LWE

Motivated by the goal of designing versatile and flexible secure computation protocols that at the same time require as little interaction as possible, we present new multiparty reusable Non-Interactive Secure Computation (mrNISC) protocols. This notion, recently introduced by Benhamouda and Lin (TCC 2020), is essentially two-round Multi-Party Computation (MPC) protocols where the first round of messages serves as a reusable commitment to the private inputs of participating parties. Using these commitments, any subset of parties can later compute any function of their choice on their respective inputs by just sending a single message to a stateless evaluator, conveying the result of the computation but nothing else. Importantly, the input commitments can be computed without knowing anything about other participating parties (neither their identities nor their number) and they are reusable across any number of desired computations. We give a construction of mrNISC that achieves standard simulation security, as classical multi-round MPC protocols achieve. Our construction relies on the Learning With Errors (LWE) assumption with polynomial modulus, and on the existence of a pseudorandom function (PRF) in $$\mathsf {NC}^1$$ . We achieve semi-malicious security in the plain model and malicious security by further relying on trusted setup (which is unavoidable for mrNISC). In comparison, the only previously known constructions of mrNISC were either using bilinear maps or using strong primitives such as program obfuscation. We use our mrNISC to obtain new Multi-Key FHE (MKFHE) schemes with threshold decryption:
Transform-Based Feature Map Compression for CNN Inference

To achieve higher accuracy in machine learning tasks, very deep convolutional neural networks (CNNs) are designed recently. However, the large memory access of deep CNNs will lead to high power consumption. A variety of hardware-friendly compression methods have been proposed to reduce the data transfer bandwidth by exploiting the sparsity of feature maps. Most of them focus on designing a specialized encoding format to increase the compression ratio. Differently, we observe and exploit the sparsity distinction between activations in earlier and later layers to improve the compression ratio. We propose a novel hardware-friendly transform-based method named 1D-Discrete Cosine Transform on Channel dimension with Masks (DCT-CM), which intelligently combines DCT, masks, and a coding format to compress activations. The proposed algorithm achieves an average compression ratio of 2.9× (53% higher than the state- of-the-art transform-based feature map compression works) during inference on ResNet-50 with an 8-bit quantization scheme.