Reviewerly/exHarmony huggingface dataset:
Bayesian hierarchical K-means clustering

Clustering algorithm is the foundation and important technology in data mining. In fact, in the real world, the data itself often has a hierarchical structure. Hierarchical clustering aims at constructing a cluster tree, which reveals the underlying modal structure of a complex density. Due to its inherent complexity, most existing hierarchical clustering algorithms are usually designed heuristically without an explicit objective function, which limits its utilization and analysis. K-means clustering, the well-known simple yet effective algorithm which can be expressed from the view of probability distribution, has inherent connection to Mixture of Gaussians (MoG). At this point, we consider combining Bayesian theory analysis with K-means algorithm. This motivates us to develop a hierarchical clustering based on K-means under the probability distribution framework, which is different from existing hierarchical K-means algorithms processing data in a single-pass manner along with heuristic strategies. For this goal, we propose an explicit objective function for hierarchical clustering, termed as Bayesian hierarchical K-means (BHK-means). In our method, a cascaded clustering tree is constructed, in which all layers interact with each other in the network-like manner. In this cluster tree, the clustering results of each layer are influenced by the parent and child nodes. Therefore, the clustering result of each layer is dynamically improved in accordance with the global hierarchical clustering objective function. The objective function is solved using the same algorithm as K-means, the Expectation-maximization algorithm. The experimental results on both synthetic data and benchmark datasets demonstrate the effectiveness of our algorithm over the existing related ones.
Authentication Protocol for Secure Automotive Systems: Benchmarking Post-Quantum Cryptography

Ensuring communication security in real-time automotive networks is of paramount importance given the sensitivity of the exchanged information and highly safety critical nature of its operation. The first step towards ensuring security is to securely authenticate all the computational nodes through use of authentication protocols based on public-key cryptography. But, traditional public-key cryptographic primitives we use today are believed to be breakable by large scale quantum computers of the future. Thus, NIST is currently running a global global level standardization process for quantum-resistant public-key cryptography, better known as post-quantum cryptography. In this work, we perform a first of its kind practical implementation of a secure authentication protocol for automotive systems with post-quantum cryptographic algorithms and perform a detailed comparative evaluation of the speed and communication bandwidth performance against their pre-quantum counterparts.
Scaling-Up Distributed Processing of Data Streams for Machine Learning

Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the learned models is essential for improved inference in these applications. Further, these applications often involve data that are either inherently gathered at geographically distributed entities or that are intentionally distributed across multiple machines for memory, computational, and/or privacy reasons. Training of models in this distributed, streaming setting requires solving stochastic optimization problems in a collaborative manner over communication links between the physical entities. When the streaming data rate is high compared to the processing capabilities of compute nodes and/or the rate of the communications links, this poses a challenging question: how can one best leverage the incoming data for distributed training under constraints on computing capabilities and/or communications rate? A large body of research has emerged in recent decades to tackle this and related problems. This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates. In particular, it focuses on methods that solve: (i) distributed stochastic convex problems, and (ii) distributed principal component analysis, which is a nonconvex problem with geometric structure that permits global convergence. For such methods, the paper discusses recent advances in terms of distributed algorithmic designs when faced with high-rate streaming data. Further, it reviews guarantees underlying these methods, which show there exist regimes in which systems can learn from distributed, streaming data at order-optimal rates.
TAM-Net: Temporal Enhanced Appearance-to-Motion Generative Network for Video Anomaly Detection

Video anomaly detection is a challenging task due to the diversity of anomaly. Existing GAN-based approaches model normal motion pattern through transforming a single image to optical flow map, which tends to learn the mapping between two adjacent frames instead of motion evolution in normal scenes. Therefore, this paper proposes a Temporal enhanced Appearance-to-Motion generative Network (TAM-Net) to model evolution of appearance and motion for normal events. In the motion generative branch, the corresponding optical flow map is generated by a ConvLSTM-based generative adversarial network from consecutive frames to learn normal motion pattern. In order to learn appearance pattern, consecutive frames are reconstructed by a auto-encoder in the reconstruction branch. Temporal encoded features of consecutive frames are shared by these two branches to represent changes of normal appearance along with time. By modeling spatio-temporal evolution of normal events, our network can effectively highlight abnormal regions with high generation errors of the predicted optical flow map and reconstructed frame. Experimental results on three independent datasets, UCSD Ped1, Ped2 and Avenue, demonstrate the competitive performance of the proposed method with the other approaches.
An Empirical Study on Regular Expression Bugs

Understanding the nature of regular expression (regex) issues is important to tackle practical issues developers face in regular expression usage. Knowledge about the nature and frequency of various types of regular expression issues, such as those related to performance, API misuse, and code smells, can guide testing, inform documentation writers, and motivate refactoring efforts. However, beyond ReDoS (Regular expression Denial of Service), little is known about to what extent regular expression issues affect software development and how these issues are addressed in practice.
Automated identification of libraries from vulnerability data

Software Composition Analysis (SCA) has gained traction in recent years with a number of commercial offerings from various companies. SCA involves vulnerability curation process where a group of security researchers, using various data sources, populate a database of open-source library vulnerabilities, which is used by a scanner to inform the end users of vulnerable libraries used by their applications. One of the data sources used is the National Vulnerability Database (NVD). The key challenge faced by the security researchers here is in figuring out which libraries are related to each of the reported vulnerability in NVD. In this article, we report our design and implementation of a machine learning system to help identify the libraries related to each vulnerability in NVD.
A Hybrid Context-aware Framework to Detect Abnormal Human Daily Living Behavior

In Ambient Assisted Living (AAL) systems, one of the main objectives is to provide intelligent services to enhance the quality of people's lives in terms of safety, well-being, and autonomy. One of the challenges in designing these systems is abnormal human behavior detection, which is critically important to prevent users, especially elderlies, from dangerous situations. Abnormality detection has been widely explored in various fields; however, challenges remain in developing effective approaches that take into account the limitations of data-driven and knowledge-driven approaches in detecting abnormal human behaviors in AAL systems. In this paper, a hybrid context-aware framework combining a machine-learning model and probabilistic reasoning is proposed to detect abnormal human behavior. An LSTM model is firstly used to classify input data into a set of labels describing human activities. Different human activity contexts, including the duration, frequency, time of the day, locations, used objects, and sequences of the frequent activities, are then extracted to analyze human behaviors. The obtained human activities and behaviors are mapped to the proposed ontology called Human AcTivity (HAT) ontology, which conceptualizes human behavior contexts. Afterward, the abnormal human behaviors are detected using Markov Logic Network (MLN), which combines logic and probability. The concepts and relationships defined in HAT ontology are exploited in defining the FOL rules used in MLN. The proposed framework is evaluated on the Orange4Home dataset and HAR dataset using smartphones. The obtained results demonstrate the ability of the proposed framework to detect abnormal human daily living behavior with high accuracy.
Conversational Interfaces for a Smart Campus

The spoken language is the most natural interface for a human being and, thanks to the scientific-technological advances made in recent decades, nowadays we have voice assistance devices to interact with a machine through the use of natural language. Vocal user interfaces (VUI) are now included in many technological devices, such as desktop and laptop computers, smartphones and tablets, navigators, and home speakers, being welcomed by the market. The use of voice assistants can also be interesting and strategic in educational contexts and in public environments. This paper presents a case study based on the design, development, and assessment of a prototype devoted to assist students' during their daily activities in a smart campus context.
A Secure Cloud Data Sharing Protocol for Enterprise Supporting Hierarchical Keyword Search

Cloud storage becomes the priority for storing and sharing data for enterprise users. Encrypting prior to uploading data to the cloud is the best way to protect business secrets, however, it hinders the convenient operations on plaintexts, such as searching over the cloud data. In addition, employees in an enterprise have multiple layer structures and a higher layer employee should have the privilege to monitor the lower layer employees’ data to check if these users violate the regulation without letting the employees be aware of. Public key encryption with keyword search (PEKS) is a well-known cryptographic primitive suitable for secure cloud storage, which supports keyword search without decryption in public key encryption settings. Unfortunately, no existing PEKS scheme supports the monitoring function without authorization from the sender. To address this issue, we propose a variant of PEKS named Hierarchical Public Key Encryption with Keyword Search (HPEKS) and provide a semi-generic construction utilizing a public key tree (PKTree) and a PEKS scheme. To better suit for the enterprise secret data sharing, we build an advanced HPEKS scheme, named designated-tester decryptable hierarchical public key encryption with keyword search (dDHPEKS), which enjoys stronger security and integrates the public key and symmetric key encryptions. We prove our dDHPEKS scheme secure under the security definition in the random oracle model. Particularly, it satisfies the security against outside offline keyword guessing attacks and furthermore, enjoys the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transparency</i> property so that the sender does not need to know the internal hierarchy structure of an enterprise in order to share encrypted data to the enterprise. Theoretical evaluation and concrete experiments show that our dDHPEKS scheme has comparable running efficiency with existing PEKS schemes.
Designing interactive sonic artefacts for dance performance

In this paper, we propose to consider the sonic interactions that occurs in a dance performance from an ecological perspective. In particular, we suggest using the conceptual models of artefact ecology and design space. As a case study, we present a work developed during a two weeks artistic residency in collaboration between a sound designer, one choreographer, and two dancers. During the residency both an interactive sound artefact based on a motion capture system, and a dance performance were developed. We present the ecology of an interactive sound artefact developed for the dance performance, with the objective to analyse how the ecology of multiple actors relate themselves to the interactive artefact.
CBR-LIME: A Case-Based Reasoning Approach to Provide Specific Local Interpretable Model-Agnostic Explanations

Research on eXplainable AI has proposed several model agnostic algorithms, being LIME [] (Local Interpretable Model-Agnostic Explanations) one of the most popular. LIME works by modifying the query input locally, so instead of trying to explain the entire model, the specific input instance is modified, and the impact on the predictions are monitored and used as explanations. Although LIME is general and flexible, there are some scenarios where simple perturbations are not enough, so there are other approaches like Anchor where perturbations variation depends on the dataset. In this paper, we propose a CBR solution to the problem of configuring the parameters of the LIME algorithm for the explanation of an image classifier. The case base reflects the human perception of the quality of the explanations generated with different parameter configurations of LIME. Then, this parameter configuration is reused for similar input images.
Human Factor on Software Quality: A Systematic Literature Review

Ensuring software quality is an important step towards a successful project. Since software development is a human-oriented process, it is possible to say that any factor affecting people will directly affect software quality and success. The aim of this study is to reveal which factors affect humans. For this purpose, we conducted a systematic literature review. We identified 80 related primary studies from the literature. We defined 7 research questions. For answering research questions, we extracted data from the primary studies. We researched human factors, methods for data collection and data analysis, publication types and years. Factors are grouped into 3 main groups: Personal factors, interpersonal factors, and organizational factors. The results show that personal factors are the most important category of human factors. It is seen that the most researched factors among personal factors are “experience” and “education”.
TapLab: A Fast Framework for Semantic Video Segmentation Tapping Into Compressed-Domain Knowledge

Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed. Recent approaches mainly devote great efforts to reducing the model size for high efficiency. In this paper, we rethink this problem from a different viewpoint: using knowledge contained in compressed videos. We propose a simple and effective framework, dubbed TapLab, to tap into resources from the compressed domain. Specifically, we design a fast feature warping module using motion vectors for acceleration. To reduce the noise introduced by motion vectors, we design a residual-guided correction module and a residual-guided frame selection module using residuals. TapLab significantly reduces redundant computations of the state-of-the-art fast semantic image segmentation models, running 3 to 10 times faster with controllable accuracy degradation. The experimental results show that TapLab achieves 70.6 percent mIoU on the Cityscapes dataset at 99.8 FPS with a single GPU card for the 1024×2048 videos. A high-speed version even reaches the speed of 160+ FPS. Code will be available soon at https://github.com/Sixkplus/TapLab.
Model Reverse-Engineering Attack using Correlation Power Analysis against Systolic Array Based Neural Network Accelerator

Various deep neural network (DNN) accelerators have been proposed for artificial intelligence (AI) inference on edge devices. On the other hand, hardware security issues of the DNN accelerator have not been discussed well. Trained DNN models are important intellectual property and a valuable target for adversaries. In particular, when a DNN model is implemented on an edge device, adversaries can physically access the device and try to reveal the implemented DNN model. Therefore, the DNN execution environment on an edge device requires countermeasures such as data encryption on off-chip memory against various reverse-engineering attacks. In this paper, we reveal DNN model parameters by utilizing correlation power analysis (CPA) against a systolic array circuit that is widely used in DNN accelerator hardware. Our experimental results show that the adversary can extract trained model parameters from a DNN accelerator even if the DNN model parameters are protected with data encryption. The results suggest that countermeasures against side-channel leaks are important for implementing a DNN accelerator on FPGA or ASIC.
Learn Chemistry with Augmented Reality

Augmented Reality (AR) has been accepted as an effective learning method which means that it becomes complementary to traditional learning, especially in chemistry. In fact, AR is an interactive experience of a real-world environment. Before recent releases of cheap and affordable smart devices, AR large-scale applications in education were almost impossible. After a brief analysis of current trends in the use of AR, we propose a new system, named ARChemistry Learning, to support the Romanian educational system. In this study, we propose a modern AR tool in the chemistry education used to support children or anyone who wants to learn chemistry, to develop logic, and to explore the world seen only on a smart device. The purpose of this research is to demonstrate how effective these AR applications are, even that in Romania they are still in a pioneering phase.
An efficient multivariate threshold ring signature scheme

At CRYPTO 2011, Sakumoto et al. introduced the first 3-pass identification protocol with security reduction to the MQ problem and impersonation probability 23. Petzoldt et al. (AAECC 2013) extended that protocol into a threshold ring signature scheme, which later was improved by Zhang and Zhao (NSS 2014). In 2015, Monteiro et al. (IEICE 2015) improved the 3-pass identification protocol of Sakumoto et al. to the one with impersonation probability 12. In this paper, we utilize the previous methods and the protocol by Monteiro et al. (2015)[20] to propose an efficient threshold ring signature. As a result, our scheme is more efficient than all previous multivariate threshold signature schemes in terms of both communication cost and signature length. In particular, the signature length of our scheme is 40% and 22% shorter than that of Petzoldt et al. and Zhang–Zhao respectively.
Influence of climatic variability on the electricity generation potential by renewable sources in the Brazilian semi-arid region

Brazil presents favorable geographic conditions for the exploitation of renewable energy sources. Although the national electricity mix is predominantly hydroelectricity-based, increasing participation of solar energy has been observed. The availability and reliability of the electricity sector are intrinsically intertwined with climate conditions and can be influenced by its changes. The study presented herein investigates the effect of observed climate changes on the potential of electricity generation from photovoltaic solar and hydroelectric sources, in the semi-arid region of the Paraiba state (Northeast Brazil). The study area includes a Small Hydroelectric Power Plant (SHPP), and solar photovoltaic plants are currently being installed. Trend analysis revealed that total rainfall increased by 3.43 mm/year for the SHPP site during the studied period; however, electricity generation decreased by 3.23 MWh/year as water use was prioritized for human and irrigation purposes. Due to intense solar radiation, the study area presents favorable opportunities for the implementation of photovoltaic solar generation technologies. The increases verified in average air temperature did not affect the solar electricity generation significantly. The consideration of climate trends can help in the planning of energy systems, as climate variability can result in considerable fluctuations in renewable electricity generation, leading to disruption in energy supply.
Automating the early detection of security design flaws

Security by design is a key principle for realizing secure software systems and it is advised to hunt for security flaws from the very early stages of development. At design-time, security analysis is often performed manually by means of either threat modeling or expert-based design inspections. However, when leveraging the wide range of established knowledge bases on security design flaws (e.g., CWE, CAWE), these manual assessments become too time consuming, error-prone, and infeasible in the context of contemporary development practices with frequent iterations. This paper focuses on design inspection and explores the potential for automating the application of inspection rules to speed up the security analysis.
Multi-task Learning for Detection and Classification of Cancer in Screening Mammography

Breast screening is an effective method to identify breast cancer in asymptomatic women; however, not all exams are read by radiologists specialized in breast imaging, and missed cancers are a reality. Deep learning provides a valuable tool to support this critical decision point. Algorithmically, accurate assessment of breast mammography requires both detection of abnormal findings (object detection) and a correct decision whether to recall a patient for additional imaging (image classification). In this paper, we present a multi-task learning approach, that we argue is ideally suited to this problem. We train a network for both object detection and image classification, based on state-of-the-art models, and demonstrate significant improvement in the recall vs no recall decision on a multi-site, multi-vendor data set, measured by concordance with biopsy proven malignancy. We also observe improved detection of microcalcifications, and detection of cancer cases that were missed by radiologists, demonstrating that this approach could provide meaningful support for radiologists in breast screening (especially non-specialists). Moreover, we argue that this multi-task framework is broadly applicable to a wide range of medical imaging problems that require a patient-level recommendation, based on specific imaging findings.