Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces

Knowledge graph embedding (KGE) is a increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this paper, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.Comment: 32 pages, 6 figure

Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.Comment: 23 pages, 16 figures, to appear in the 45th IEEE Symposium on Security and Privacy (S&P'24

Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network

Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world—the input stimuli—into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility—the output phenotypes. How does the ‘uninformed’ process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions

Crikvenica\u27s cemetries

Autor u radu donosi povijesni pregled posljednjih počivališta, od najstarijih prapovijesnih nalaza na predjelu Stolnič, nalaza iz razdoblja antike u sklopu rimskoga Ad Turresa, srednjovjekovnog groblja Stranče-Gorica u bliskom zaleđu crikvenice, zatim tri groblja vezana uz crkve: sv. Šimuna i Jude Tadeja u naselju Kotor, sv. Antona na Gorici i pavlinske Crkve BDM na ušću Dubračine, jednog kratkotrajno korištenog groblja na lokaciji današnjeg Gradskog kupališta, do sadašnjega i budućega crikveničkog groblja koje postoji tek u planskim dokumentima. Povijest crikveničkih groblja je zapravo povijest samoga mjesta Crikvenice i njegove urbanizacije. S porastom broja stanovnika, koji se u posljednjih 150 godina povećao za gotovo tri i pol puta, rastu potrebe za novim ukopištima. Stoga se koncem 18. i početkom 19. stoljeća otvaraju tri groblja na novim lokacijama, ali se iz urbanističkih razloga dva groblja zatvaraju, a ukopi sele na sadašnje crikveničko groblje. Prostorni resursi postojećeg groblja su do krajnosti potrošeni, pa je izrađena planska dokumentacija za novo groblje u blizini crikveničkog naselja Zoričići, ispod vrha Drenin.In the paper the author gives an overview of the final resting places, from the oldest prehistoric finds at the area of Stolnič, finds from Antiquity within the Roman Ad Turres, the Stranče-Gorica mediaeval cemetery in the nearby hinterland of Crikvenica, then the three cemeteries connected to the churches: Saints Simon and Jude Thaddeus in the settlement of Kotor, St Anthony on Gorica and the Pauline Church of the Blessed Virgin Mary at the mouth of the Dubračina, a single use cemetery at the site of the town’s current bathing area, to the present-day and future Crikvenica cemetery which exists only in planning documents. The history of Crikvenica’s cemeteries is in fact the history of the town of Crikvenica itself and its urbanisation. With the growth of the population, which over the last 150 years has increased almost three and a half times, the need for new burial grounds has also grown. Therefore, at the end of the 18th century and the beginning of the 19th three cemeteries opened in new locations, however for urbanisation reasons two cemeteries were closed, and the burials moved to the current Crikvenica cemetery. The spatial resources of the existing cemetery have been used to the extremes, so planning documentation has been made for a new cemetery near to the Crikvenica settlement of Zoričići, under the hilltop of Drenin

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe

Automatic Detection of Repetitive Components in 3D Mechanical Engineering Models

We present an intelligent method to automatically detect repetitive components in 3D mechanical engineering models. In our work, a new Voxel-based Shape Descriptor (VSD) is proposed for effective matching, based on which a similarity function is defined. It uses the voxels intersecting with 3D outline of mechanical components as the feature descriptor. Because each mechanical component may have different poses, the alignment before the matching is needed. For the alignment, we adopt the genetic algorithm to search for optimal solution where the maximum global similarity is the objective. Two components are the same if the maximum global similarity is over a certain threshold. Note that the voxelization of component during feature extraction and the genetic algorithm for searching maximum global similarity are entirely implemented on GPU; the efficiency is improved significantly than with CPU. Experimental results show that our method is more effective and efficient than that existing methods for repetitive components detection

A Potential Utility of Peer Prediction Method to Consensus Building on Decentralized Oracle Systems

In this paper, we pointed the potential utility of peer prediction method to the existing consensus building in decentralized oracle systems where participants aim to verify the validity of input information to blockchain without relying on a trusted third party （TTP）. This is important because, despite the recent expectation of implementing decentralized oracle systems, few discussions have dealt with the incentive design for their consensus building, much less the synergy with peer prediction method. Specifically, we mentioned the followings through the survey of preceding studies:（i）the current predominant method of staking that allows validators to bet the reward tokens has the limitations such as a vulnerability to strategic behavior and a lack of incentive to participate in the verification,（ii） these problems could be solved by peer prediction method which determines the amount of rewards based on the posterior probability distribution on the report of others updated by one’s own report. Peer prediction method can encourage validators to perform proper verification while supplementing the token-based rewards, and thereby can contribute to the realization of the mining mechanism based on subjective review instead of computational resources. On the other hand, several obstacles still remain to propose a practical incentive design, such as the fluctuation of token price that would prevent peer prediction from incentivizing proper verification.査読研究論文Refereed Paper