Set Of Primitives Research Articles

Improving the Scalability of GPU Synchronization Primitives

General-purpose GPU applications increasingly use synchronization to enforce ordering between many threads accessing shared data. Accordingly, recently there has been a push to establish a common set of GPU synchronization primitives. However, the expressiveness of existing GPU synchronization primitives is limited. In particular the expensive GPU atomics often used to implement fine-grained synchronization make it challenging to implement efficient algorithms. Consequently, as GPU algorithms scale to millions or billions of threads, existing GPU synchronization primitives either scale poorly or suffer from livelock or deadlock issues because of heavy contention between threads accessing shared synchronization objects. We seek to overcome these inefficiencies by designing more efficient, scalable GPU barriers and semaphores. In particular, we show how multi-level sense reversing barriers and priority mechanisms for semaphores can be designed with the GPUs unique processing model in mind to improve performance and scalability of GPU synchronization primitives. Our results show that the proposed designs significantly improve performance compared to state-of-the-art solutions like CUDA Cooperative Groups and optimized CPU-style synchronization algorithms at medium and high contention levels, scale to an order of magnitude more threads, and avoid livelock in these situations unlike prior open source algorithms. Overall, across three modern GPUs the proposed barrier algorithm improves performance by an average of 33% over a GPU tree barrier algorithm and improves performance by an average of 34% over CUDA Cooperative Groups for five full-sized benchmarks at high contention levels; the new semaphore algorithm improves performance by an average of 83% compared to prior GPU semaphores.

Aggressive Design Reuse for Ubiquitous Zero-Trust Edge Security—From Physical Design to Machine-Learning-Based Hardware Patching

This work presents an overview of challenges and solid pathways toward ubiquitous and sustainable hardware security in next-generation silicon chips at the edge of distributed and connected systems (e.g., IoT and AIoT). As the first challenge, the increasingly connected nature and the exponential proliferation of edge devices are unabatingly increasing the overall attack surface, making attacks easier and mandating ubiquitous security down to each edge node. At the same time, the necessity to incorporate zero-trust policies in large-scale distributed systems requires a complete set of security primitives for hardware-backed authentication, and a higher degree of physical context awareness (including primitives detecting the onset of physical attacks). Thus, making the inclusion of such security primitives economically sustainable even in low-end devices is a second key challenge. As third challenge, the ever-changing vulnerability landscape and the need for increased chip longevity in distributed systems require security assurance methods that are sustainable and adaptive across the entire chip lifecycle. In this work, design principles and promising directions to enable ubiquitous and sustainable security capabilities along with physical awareness are discussed. Such achievements require a fundamental rethinking of design methodologies to enable aggressive design and resource reuse (e.g., area, power, and design effort), along with low-cost on-chip sensorization and intelligence for physical attack detection. Such rethinking inevitably crosses over the traditional design abstractions, and requires innovation from the physical to the algorithmic level. At the physical and circuit levels, design and resource reuse is enabled by immersed-in-logic and in-memory security approaches. At the algorithm level, “hardware patching” is introduced and exemplified to show that runtime intelligence (machine learning) allows security capabilities to adapt and improve over time, as typical of security patching in software. Sensing techniques to detect attacks in situ from noninvasive to invasive are illustrated while still preserving fully automated design approaches. Overall, the above design principles are expected to push security capabilities in distributed systems to a new level, ultimately making the edge more intelligent and self-reliant, and security measures more distributed.

Secure Authentication in the Smart Grid

Authenticated key agreement is a process in which protocol participants communicate over a public channel to share a secret session key, which is then used to encrypt data transferred in subsequent communications. LLAKEP, an authenticated key agreement protocol for Energy Internet of Things (EIoT) applications, was recently proposed by Zhang et al. While the proposed protocol has some interesting features, such as putting less computation on edge devices versus the server side, its exact security level is unclear. As a result, we shed light on its security in this paper through careful security analysis against various attacks. Despite the designers’ security claims in the random oracle model and its verification using GNY logic, this study demonstrates that this protocol has security weaknesses. We show that LLAKEP is vulnerable to traceability, dictionary, stolen smart glass, known session-specific temporary information, and key compromise impersonation attacks. Furthermore, we demonstrate that it does not provide perfect forward secrecy. To the best of our knowledge, it is the protocol’s first independent security analysis. To overcome the LLAKEP vulnerabilities, we suggested the LLAKEP+ protocol, based on the same set of cryptographic primitives, namely the one-way hash function and ECC point multiplication. Our comprehensive security analysis demonstrates its resistance to different threats, such as impersonation, privileged insider assaults, and stolen smart glass attacks, along with its resistance to sophisticated assaults, such as key compromised impersonation (KCI) and known session-specific temporary information (KSTI). The overhead of the proposed protocol is acceptable compared to the provided security level.

MOWL: Python library for machine learning with biomedical ontologies.

Ontologies contain formal and structured information about a domain and are widely used in bioinformatics for annotation and integration of data. Several methods use ontologies to provide background knowledge in machine learning tasks, which is of particular importance in bioinformatics. These methods rely on a set of common primitives that are not readily available in a software library; a library providing these primitives would facilitate the use of current machine learning methods with ontologies and the development of novel methods for other ontology-based biomedical applications. We developed mOWL, a Python library for machine learning with ontologies formalized in the Web Ontology Language (OWL). mOWL implements ontology embedding methods that map information contained in formal knowledge bases and ontologies into vector spaces while preserving some of the properties and relations in ontologies, as well as methods to use these embeddings for similarity computation, deductive inference and zero-shot learning. We demonstrate mOWL on the knowledge-based prediction of protein-protein interactions using the gene ontology and gene-disease associations using phenotype ontologies. mOWL is freely available on https://github.com/bio-ontology-research-group/mowl and as a Python package in PyPi. Supplementary data are available at Bioinformatics online.

Primitive quantum gates for an SU(2) discrete subgroup: Binary tetrahedral

We construct a primitive gate set for the digital quantum simulation of the binary tetrahedral ($\mathbb{BT}$) group on two quantum architectures. This non-Abelian discrete group serves as a crude approximation to $SU(2)$ lattice gauge theory while requiring five qubits or one quicosotetrit per gauge link. The necessary basic primitives are the inversion gate, the group multiplication gate, the trace gate, and the $\mathbb{BT}$ Fourier transform over $\mathbb{BT}$. We experimentally benchmark the inversion and trace gates on ibm_nairobi, with estimated fidelities between 14%--55%, depending on the input state.

A model-based approach for vulnerability analysis of IoT security protocols: The Z-Wave case study

IoT (Internet of Things) devices are extensively used in security-critical services, as for example home door opening, gas monitoring, alarm systems, etc. Often, they use communication protocols with no standardisation and no security guarantee. Unsecured use of connected devices can cause threats or damages to the users, so security assurance, which can be ensured by the use of formal methods, must be guaranteed. Unfortunately practical usage of formal methods during the protocol design is very limited or missing at all.To address the problem of providing the designer with a user-friendly but rigorous design approach based on the use of formal methods, supporting security assurance already at the model level, but hiding the complexity of formal notations and verification techniques, in this paper we propose an approach, based on the Abstract State Machine formal method, for the specification and verification of security protocols. Specifically, we introduce a set of built-in primitives to model communication protocols and their security properties. Security verification can be carried out under the hypothesis of either a passive or an active attacker.The effectiveness of this approach is shown by means of its application to the Z-Wave protocol, claimed to be one of the most secure protocol for IoT devices communication thanks to the addition of the S2 Security class. We show the formal specification of the Z-Wave protocol and the security verification process.

Discontinuous Galerkin method with Voronoi partitioning for quantum simulation of chemistry

To circumvent a potentially dense two-body interaction tensor and obtain lower asymptotic costs for quantum simulations of chemistry, the discontinuous Galerkin (DG) basis set with a rectangular partitioning strategy was recently introduced [McClean et al, New J. Phys. 22, 093015, 2020]. We propose and numerically scrutinize a more general DG basis set construction based on a Voronoi decomposition with respect to the nuclear coordinates. This allows the construction of DG basis sets for arbitrary molecular and crystalline configurations. We here employ the planewave dual basis set as primitive basis set in the supercell model; as a set of grid-based nascent delta functions, the planewave dual functions provide sufficient flexibility for the Voronoi partitioning. The presented implementation of this DG-Voronoi approach is in Python and solely based on PySCF. We numerically investigate the performance, at the mean-field and correlated level of theory for quasi-1D, quasi-2D and fully 3D systems, and exemplify the application to crystalline systems.

A language of thought for the mental representation of geometric shapes

In various cultures and at all spatial scales, humans produce a rich complexity of geometric shapes such as lines, circles or spirals. Here, we propose that humans possess a language of thought for geometric shapes that can produce line drawings as recursive combinations of a minimal set of geometric primitives. We present a programming language, similar to Logo, that combines discrete numbers and continuous integration to form higher-level structures based on repetition, concatenation and embedding, and we show that the simplest programs in this language generate the fundamental geometric shapes observed in human cultures. On the perceptual side, we propose that shape perception in humans involves searching for the shortest program that correctly draws the image (program induction). A consequence of this framework is that the mental difficulty of remembering a shape should depend on its minimum description length (MDL) in the proposed language. In two experiments, we show that encoding and processing of geometric shapes is well predicted by MDL. Furthermore, our hypotheses predict additive laws for the psychological complexity of repeated, concatenated or embedded shapes, which we confirm experimentally.

Terracini Loci of Segre Varieties

Fix a format (n1+1)×⋯×(nk+1), k>1, for real or complex tensors and the associated multiprojective space Y. Let V be the vector space of all tensors of the prescribed format. Let S(Y,x) denote the set of all subsets of Y with cardinality x. Elements of S(Y,x) are associated to rank 1 decompositions of tensors T∈V. We study the dimension δ(2S,Y) of the kernel at S of the differential of the associated algebraic map S(Y,x)→PV. The set T1(Y,x) of all S∈S(Y,x) such that δ(2S,Y)>0 is the largest and less interesting x-Terracini locus for tensors T∈V. Moreover, we consider the one (minimally Terracini) such that δ(2A,Y)=0 for all A⊈S. We define and study two different types of subsets of T1(Y,x) (primitive Terracini and solution sets). A previous work (Ballico, Bernardi, and Santarsiero) provided a complete classification for the cases x=2,3. We consider the case x=4 and several extremal cases for arbitrary x.

Multi-disciplinary optimization of a space re-entry vehicle using skeleton-based integral soft objects

In the present paper, a parametric procedure for the shape modelling and optimization of a next generation winged re-entry vehicle using Skeleton-Based Integral Soft Objects is presented. Detailed aerodynamic bodies are efficiently generated just “inflating” a parametric flat domain representing the aeroshape planform composed by discretized panels. Specifically, a potential field generated by a set of mathematical primitives and related field functions is used to alter nodal coordinates of the flat planform domain. In such mode, several geometrical features as canopy, cabin outline, dihedral angles, winglets, and vertical tails can be easily modelled, added, or suppressed. Specialized Skeleton-Based Integral Soft Objects operators are used to produce both smooth and sharp transitions between different sub-component surfaces as required. Consequently, huge geometrical variations of the aerodynamic shape can be then efficiently generated using a very limited number of high-sensitivity parameters. The suggested modelling procedure has been tested performing several multi-disciplinary shape optimizations driven by rapid engineering evaluators, for a minimum mass/maximum cross range design of a concept vehicle that re-enters from Low Earth Orbit, up to a controlled horizontal landing on runway. Additionally, Computational Fluid Dynamics evaluations have been performed on optimum design candidates to validate the real performance of the proposed aeroshape configurations. Results comparison has highlighted slight differences within the range 5-10%, thus confirming the reliability of the design of the aircraft aeroshape.

A Grammatically and Structurally Based Part of Speech (POS) Tagger for Arabic Language

In this paper we report on an experimental syntactically and morphologically driven rule-based Arabic tagger. The tagger is developed using Arabic language grammatical rules and regulations. The tagger requires no pre-tagged text and is developed using a primitive set of lexicon items along with extensive grammatical and structural rules. It is tested and compared to Stanford tagger both in terms of accuracy and performance (speed). Obtained results are quite comparable to Stanford tagger performance with marginal difference favoring the developed tagger in accuracy with huge difference in terms of speed of execution. The newly developed tagger named MTE Tagger has been tested and evaluated. For the evaluation of its accuracy of tagging, a set of Arabic text was manually prepared and annotated. Compared to Stanford tagger, the MTE tagger performance was quite comparable. The developed tagger makes use of no pre-annotated datasets, except of some simple lexicon consisting of list of words representing closed word types like demonstrative nouns or pronouns or some particles. For the purpose of evaluation of the new tagger, it was run on multiple datasets and results were compared to those of Stanford tagger. In particular, both taggers (the MTE and the Stanford) were run on a set of 1226 sentences with close to 20,000 tokens that was human annotated and verified to serve as testbed. The results were very encouraging where in both test runs, the MTE tagger outperformed the Stanford tagger in terms of accuracy of 87.88% versus 86.67% for the Stanford tagger. In terms of speed of tagging and in comparison Stanford tagger, MTE Taggers’ performance was on average 1:50. More improved accuracy is possible in future work as the set of rules are further optimized, integrated and more of Arabic language properties such as end of word discretization are used.

SEAF: A Scalable, Efficient, and Application-independent Framework for container security detection

Container technology has become a popular development that can conveniently accelerate building, running, and sharing applications. However, a container image packaging a collection of software usually lurks various defects threatening consumer safety, such as embedded malware, software vulnerability, privacy leakage, etc. Moreover, developers and users share container images through a centralized, public, and massive repository (e.g., Docker Hub), which can magnify the impact of these security defects in a fast-spreading way. Unfortunately, existing detection methods cannot effectively or efficiently discover such hidden flaws among the numerous images.This paper proposes a novel method to effectively detect and measure container security flaws embedded in images. Based on the crucial insight that container images are constructed hierarchically, each image depends on layers of forwarding image and adds updated content in layers of itself. Our work mines a Global Relationship Tree (GRT) based on dependency among the images that contain common layers. Meanwhile, by traversing the GRT and leveraging content differential analysis, we can locate the changing content in an image corresponding to defects. Therefore, when checking flaws among numerous images, we make a layer-sensitive detection by reusing common layers’ detection results in iterative processes to boost detection and accurately measure the influence scope of defects. Finally, we summarize and develop a set of detection primitives for scaling our approach to handle various flaws that may lead to multiple risks in potential.Depending upon this method, we implemented SEAF, a Scalable, Efficient, and Application-independent Framework, and evaluated it on popular images of diverse applications in Docker Hub. The experiment result shows that SEAF can discover different security flaws fast. Compared to the state-of-the-art tool, Clair, SEAF is more efficient and can find significantly more types of defects.

Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics

The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.

IntStream: Towards Flexible, Expressive, and Scalable Network Telemetry

Due to the complexity of the network structure and the high growth of the transmission speed, the measurement and management of the network are facing serious challenges. The traditional bottom-up network telemetry methods are no longer applicable to complex network scenarios. To bridge this gap, we propose IntStream, a flexible, expressive and scalable network telemetry framework to allow network operators to measure and analyze network through passive stream processing and active probing. However, there are three key challenges to building an intent-based telemetry system: (1) The diversity of network data sources. (2) The complexity of the measurement tasks. (3) The low overhead requirements of the telemetry system. IntStream introduces a lightweight component to extract and parse data from various data sources and divides the data stream processing into local and global stages. IntStream provides a set of rich expressive primitives to support operators to write telemetry tasks based on intent. By performing part of the telemetry task on the local stage, the transmission overhead of intermediate data can be effectively reduced. The evaluation results conducted on a large campus network show that IntStream can support a wide range of telemetry tasks while reducing the intermediate data transmission overhead by 99.31% on average.

Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems

In critical infrastructure applications, timely and consistent fault detection and diagnosis is an increasingly important operational process, especially in the energy sector where safety is of the utmost importance. To realise this, engineers have to manually analyse data acquired from several assets using predefined diagnostic processes, but this is a time-consuming process requiring significant amounts of specialist expert knowledge. Data-driven approaches to support fault detection and diagnosis, and other similar problems, can produce accurate results comparable to what the engineers can achieve in a fraction of the time. However, the majority of these data-driven techniques are black box techniques and lack explainability which is often necessary for explaining decisions about critical assets in the power generation industry. Knowledge-based systems, such as rule-based expert systems have been shown to provide not only accurate decisions but also the explanation and reasoning behind these decisions in some related applications. However, there is a significant time cost associated with the development of knowledge-based systems, and in particular with the knowledge elicitation process, where the domain expert’s knowledge is formalised and is encoded into the system. This challenge is commonly referred to as the knowledge elicitation bottleneck.In this paper, we present a novel approach to performing the knowledge elicitation using a set of symbolic primitives (rise, fall, fluctuate, and stable) to parameterise typical time-series condition monitoring data. The knowledge is represented by using a common language that can easily be communicated with (and from) the domain experts. This allows for the quick and accurate elicitation of the domain experts knowledge, but also the formalisation and implementation of the knowledge into a rapidly produced diagnostic system. Further to this, due to the parametrisation of the knowledge, it is possible to iteratively improve the knowledge base by updating these parameters based on new unseen data. This approach was applied to the Tennessee Eastman dataset, which is simulated data of a real-world industrial process. It was found that by using this approach it was possible to accurately and quickly capture the knowledge required to detect several faults within the case study dataset, but also provided fully explained reasons why each fault was detected by relating the explanations to the symbolic primitives previously defined.

Tom Thomson’s Islands, Canoe Lake, 1916: a rock as the head of a bear

Abstract Tom Thomson is a doyen of Canadian art. Here, I argue one of his well-known pictures contains a hidden figure. In Thomson’s Islands, Canoe Lake, 1916, a blue-gray picture primitive richly affords a rock and a bear, a long-overlooked ambiguity, one that Thomson did not tell us he intended. Its picture primitives are contours and patches. They offer a limited set of scene primitives. A likelihood ratio of less than 1 in 100 supports the contention that the bear is a hidden figure.

An Evolutionary Computation Approach for Twitter Bot Detection

Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enables us to discover a human-readable detection algorithm that exhibits a detection accuracy close to the top state-of-the-art methods on the TwiBot-20 dataset while providing predictions that can be interpreted, and whose uncertainty can be easily measured. To the best of our knowledge, this work is the first attempt at adopting evolutionary computation techniques for detecting bot profiles on social media platforms.

Translated sums of primitive sets

The Erd\H{o}s primitive set conjecture states that the sum $f(A) = \sum_{a\in A}\frac{1}{a\log a}$, ranging over any primitive set $A$ of positive integers, is maximized by the set of prime numbers. Recently Laib, Derbal, and Mechik proved that the translated Erd\H{o}s conjecture for the sum $f(A,h) = \sum_{a\in A}\frac{1}{a(\log a+h)}$ is false starting at $h=81$, by comparison with semiprimes. In this note we prove that such falsehood occurs already at $h= 1.04\cdots$, and show this translate is best possible for semiprimes. We also obtain results for translated sums of $k$-almost primes with larger $k$.

Realism and Power in Mark Twain's

Realism and Power in Mark Twain's

Controlling Palm-Object Interactions Via Friction for Enhanced In-Hand Manipulation

In-hand manipulation is necessary, yet challenging for robotic hands as robots begin to interact with real-world objects and environments. The intentional incorporation of an active palm can lead to improved dexterity. In this work, we demonstrate how controlling the palm-object interaction enables access to different sets of motion primitives. We first discuss how various properties of the palm affect in-hand manipulation, as well as grasping. Here, we focus on varying the friction force between the object and the palm by either changing the coefficient of friction or the normal load. We explore how these two design factors can be actively controlled to enable 5-degree-of-freedom object motion within the hand. We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variable-friction</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variable-preload</i> palms that enable on-the-fly switching between in-plane and out-of-plane manipulation modes. Additionally, we discuss their impact on grasp stability. Finally, we demonstrate how an object can be translated and pivoted within the hand by sequentially performing synchronized palm and finger actions.