GITEX AI Europe is an annual technology trade show and conference held in Berlin, Germany, as part of GITEX GLOBAL. The event focuses on the European technology market, specifically in the sectors of artificial intelligence (AI), cybersecurity, quantum computing, and digital infrastructure. The event is organized by Kaoun International GmbH, the international arm of the Dubai World Trade Centre (DWTC), in partnership with Messe Berlin. == History == The establishment of GITEX AI Europe was announced in 2023 as part of a strategic move to bring the GITEX brand to the European market. The inaugural edition took place from May 21 to 23, 2025, at the Messe Berlin exhibition grounds. The launch was supported by the Berlin Senate and the German Federal Ministry for Economic Affairs and Climate Action. The first edition of GITEX AI Europe in 2025 featured 21,650 attendees, 1,434 exhibiting companies, and 755 startups, with 513 speakers representing 125 countries. The next edition is scheduled for June 30 – July 1, 2026 in Berlin. == Program == The event consists of an exhibition floor for corporate displays, several conference stages for keynote speeches, and specialized sub-events. The conference program includes tracks such as "AI Stack Sovereignty," "Cyber Regulation & Trust Convergence," and "Institutional Growth Capital." GITEX AI Europe incorporates brands under its umbrella: AI Everything Europe: Focused on the development and application of generative AI and machine learning. North Star Europe: A dedicated program for startups and venture capital, featuring the "Supernova Challenge" pitch competition. GISEC Europe: A cybersecurity forum discussing regulation and infrastructure defense. GITEX Quantum Expo: Focused on the commercialization of quantum computing. Institutional partners for the event include the German Federal Ministry for Economic Affairs and Climate Action, the European Innovation Council (EIC), the International Telecommunication Union (ITU), Bitkom, and Digital Dubai.
Sentence extraction
Sentence extraction is a technique used for automatic summarization of a text. In this shallow approach, statistical heuristics are used to identify the most salient sentences of a text. Sentence extraction is a low-cost approach compared to more knowledge-intensive deeper approaches which require additional knowledge bases such as ontologies or linguistic knowledge. In short, sentence extraction works as a filter that allows only meaningful sentences to pass. The major downside of applying sentence-extraction techniques to the task of summarization is the loss of coherence in the resulting summary. Nevertheless, sentence extraction summaries can give valuable clues to the main points of a document and are frequently sufficiently intelligible to human readers. == Procedure == Usually, a combination of heuristics is used to determine the most important sentences within the document. Each heuristic assigns a (positive or negative) score to the sentence. After all heuristics have been applied, the highest-scoring sentences are included in the summary. The individual heuristics are weighted according to their importance. === Early approaches and some sample heuristics === Seminal papers which laid the foundations for many techniques used today have been published by Hans Peter Luhn in 1958 and H. P Edmundson in 1969. Luhn proposed to assign more weight to sentences at the beginning of the document or a paragraph. Edmundson stressed the importance of title-words for summarization and was the first to employ stop-lists in order to filter uninformative words of low semantic content (e.g. most grammatical words such as of, the, a). He also distinguished between bonus words and stigma words, i.e. words that probably occur together with important (e.g. the word form significant) or unimportant information. His idea of using key-words, i.e. words which occur significantly frequently in the document, is still one of the core heuristics of today's summarizers. With large linguistic corpora available today, the tf–idf value which originated in information retrieval, can be successfully applied to identify the key words of a text: If for example the word cat occurs significantly more often in the text to be summarized (TF = "term frequency") than in the corpus (IDF means "inverse document frequency"; here the corpus is meant by document), then cat is likely to be an important word of the text; the text may in fact be a text about cats.
Shape table
Shape tables are a feature of the Apple II ROMs which allows for manipulation of small images encoded as a series of vectors. An image (or shape) can be drawn in the high-resolution graphics mode—with scaling and rotation—via software routines in the ROM. Shape tables are supported via Applesoft BASIC and from machine code in the "Programmer's Aid" package that was bundled with the original Integer BASIC ROMs for that computer. Applesoft's high-resolution graphics routines were not optimized for speed, so shape tables were not typically used for performance-critical software such as games, which were typically written in assembly language and used pre-shifted bitmap shapes. Shape tables were used primarily for static shapes and sometimes for fancy text; Beagle Bros offered a number of fonts in Font Mechanic as Applesoft shape tables. == Technical details == The vectors of a two-dimensional graphic, each encoding a direction from the previous pixel along with a flag indicating whether the new pixel should be illuminated or not, were encoded up to three in a byte. These were stored in a table via the Monitor or the POKE command. From there, the graphic could be referenced by number (a table could contain up to 255 shapes), and built-in Applesoft routines permitted scaling, rotating, and drawing or erasing the shape. An XOR mode was also available to allow the shape to be visible on any color background; this had the advantage, also, of allowing the shape to be easily erased by redrawing it. Apple did not provide any utilities for creating shape tables; they had to be created by hand, usually by plotting on graph paper, then calculating the hexadecimal values and entering them into the computer. Beagle Bros created a shape table editing program, which eliminated the "number crunching", called Apple Mechanic, and a related program, Font Mechanic.
Fragment (computer graphics)
In computer graphics, a fragment is the data necessary to generate a single pixel's worth of a drawing primitive in the frame buffer. These data may include, but are not limited to: raster position depth interpolated attributes (color, texture coordinates, etc.) stencil alpha window ID As a scene is drawn, drawing primitives (the basic elements of graphics output, such as points, lines, circles, text etc.) are rasterized into fragments which are textured and combined with the existing frame buffer. How a fragment is combined with the data already in the frame buffer depends on various settings. In a typical case, a fragment may be discarded if it is further away than the pixel which is already at that location (according to the depth buffer). If it is nearer than the existing pixel, it may replace what is already there, or, if alpha blending is in use, the pixel's color may be replaced with a mixture of the fragment's color and the pixel's existing color, as in the case of drawing a translucent object. In general, a fragment can be thought of as the data needed to shade the pixel, plus the data needed to test whether the fragment survives to become a pixel (depth, alpha, stencil, scissor, window ID, etc.). Shading a fragment is done through a fragment shader (or pixel shaders in Direct3D). In computer graphics, a fragment is not necessarily opaque, and could contain an alpha value specifying its degree of transparency. The alpha is typically normalized to the range of [0, 1], with 0 denotes totally transparent and 1 denotes totally opaque. If the fragment is not totally opaque, then part of its background object could show through, which is known as alpha blending.
AI content watermarking
AI content watermarking is the process of embedding imperceptible yet detectable signals into content generated by artificial intelligence systems, such as text, images, audio, or video. The technique allows the content to be traced and identified as machine-generated without compromising its quality for the end user. AI watermarking has emerged as a key approach to address growing concerns about misinformation, deepfakes, copyright infringement, and the traceability of synthetic content in the context of the rapid development of generative artificial intelligence. Unlike traditional visible watermarks used in photography, AI content watermarks are typically invisible to humans and can only be detected and deciphered algorithmically. The concept is distinct from the watermarking of AI models themselves (to prevent model theft) and from the watermarking of training data (to combat unauthorized data use). Modern AI watermarking schemes are typically formalized as a pair of algorithms, an embedding (or generation) algorithm and a detection algorithm, sharing a secret key, whose performance is evaluated along three competing axes: quality (the watermark must not noticeably degrade outputs), detectability (the watermark must be statistically distinguishable from unwatermarked content), and robustness (the watermark must persist under adversarial or incidental modifications). == Background == Digital watermarking has been used for decades to protect physical and digital media, from paper currency to photographs. Classical schemes typically embedded a fixed bit-string into a fixed cover signal, with robustness criteria defined against a small fixed set of distortions such as JPEG compression or additive Gaussian noise. The rapid advancement of generative AI in the early 2020s, however, created a new and qualitatively different demand: rather than protecting a single artifact, watermarks for AI content must be embedded automatically across an open-ended distribution of generated outputs while remaining robust to a much wider class of adversarial transformations, including paraphrasing, image regeneration via diffusion models, and re-recording. Large image generation models such as DALL-E, Stable Diffusion, and Midjourney, along with large language models like ChatGPT, made it possible to produce highly realistic synthetic text, images, audio, and video at scale, raising significant ethical and security concerns. In July 2023, the Biden administration secured voluntary commitments from leading AI companies, including OpenAI, Alphabet, Meta, and Amazon, to develop watermarking and other provenance technologies to help users identify AI-generated content. == Formal definitions and design goals == Most modern AI watermarking schemes can be formalized as a pair of algorithms ( W m , D e t e c t ) {\displaystyle ({\mathsf {Wm}},{\mathsf {Detect}})} parameterized by a secret key k {\displaystyle k} . The embedding algorithm W m {\displaystyle {\mathsf {Wm}}} takes a generative model M {\displaystyle M} (and optionally a prompt) and returns a watermarked output x {\displaystyle x} ; the detection algorithm D e t e c t ( x , k ) {\displaystyle {\mathsf {Detect}}(x,k)} outputs a real-valued score (typically a p-value or log-likelihood ratio) used to decide whether x {\displaystyle x} was produced by the watermarked generator. The literature evaluates such schemes along several largely conflicting criteria: Criteria for evaluation include imperceptibility or quality preservation, measured for text via perplexity and human preference judgments, and for images and audio via metrics such as PSNR, SSIM, LPIPS, or PESQ. Detectability is typically expressed as the true positive rate at a fixed false positive rate (e.g. 1% or 10^-6), or as the number of tokens or pixels needed to reach a given confidence level. Robustness refers to the requirement that the watermark should survive expected modifications like JPEG or MP3 compression, cropping, noise, paraphrasing, or machine translation. Distortion-freeness is a stronger property requiring that the marginal distribution of any single watermarked output be statistically identical to the unwatermarked model's distribution. Schemes due to Aaronson, Christ et al., and Kuditipudi et al. are distortion-free in this sense, while the original Kirchenbauer et al. scheme is not. Forgery resistance or unforgeability means an adversary without the secret key should be unable to produce content that passes detection. == Techniques == AI watermarking techniques vary significantly depending on the type of content being watermarked. At its core, the process involves two main stages: embedding (or encoding) the watermark, and detection. There are two primary methods for embedding: watermarking during content generation, which requires access to the AI model itself but is generally more robust, and post-generation watermarking, which can be applied to content from any source, including closed-source models. Watermarks can be broadly classified as visible, including overt marks such as logos or text overlays, or imperceptible, which are detectable only by algorithms. They can also be classified by durability: robust watermarks are designed to withstand common transformations such as compression, cropping, and re-encoding, while fragile watermarks are easily destroyed by any alteration, making them useful for tamper detection. A further axis distinguishes zero-bit watermarks, which only signal "this content was generated by model M," from multi-bit watermarks, which embed an arbitrary payload (such as a user identifier) that can be recovered at detection time. === Text === Text watermarking is considered one of the most challenging modalities because natural language offers relatively limited redundancy compared to images or audio. Modern approaches for large language models alter the autoregressive sampling process so that some statistical signature is left in the choice of tokens, while leaving the surface form of the text unchanged. The literature distinguishes three main families of generation-time text watermarks. Logit-biasing schemes (e.g. KGW) add a fixed bias δ {\displaystyle \delta } to a pseudorandomly selected subset of vocabulary logits before softmax sampling. Reweighting or sampling-based schemes (e.g. SynthID-Text) compose multiple pseudorandom tournaments over the model's full distribution. Distortion-free schemes based on the Gumbel-max trick or inverse transform sampling (Aaronson 2022; Kuditipudi et al. 2023; Christ et al. 2024) preserve the marginal output distribution of the model. ==== KGW: token-probability shifting ==== The pioneering "green list / red list" scheme of Kirchenbauer et al. (KGW), introduced at ICML 2023, is the foundation for most subsequent text watermarks. At each decoding step t {\displaystyle t} , a pseudorandom function (PRF) keyed by a secret k {\displaystyle k} is applied to a context window of h {\displaystyle h} previous tokens to deterministically partition the vocabulary V {\displaystyle V} of size N {\displaystyle N} into a "green list" G ⊂ V {\displaystyle G\subset V} of size γ N {\displaystyle \gamma N} and its complement, the "red list" R = V ∖ G {\displaystyle R=V\setminus G} , where γ ∈ ( 0 , 1 ) {\displaystyle \gamma \in (0,1)} (typically γ = 1 / 2 {\displaystyle \gamma =1/2} ) is the green fraction. A logits processor then increments every green-list logit by a fixed bias δ > 0 {\displaystyle \delta >0} before softmax: ℓ v ′ = ℓ v + δ ⋅ 1 [ v ∈ G ] {\displaystyle \ell '_{v}=\ell _{v}+\delta \cdot \mathbf {1} [v\in G]} so that, after sampling, green tokens are over-represented but generation is not constrained to green tokens alone; high-entropy positions tolerate the bias gracefully, while low-entropy positions (where one token dominates the logits) override the watermark and preserve correctness on factual content. Detection requires only the secret key and the candidate text, not the language model itself. The detector recomputes the partition g ( ⋅ ) {\displaystyle g(\cdot )} for each token, counts the number of green hits | G | hits {\displaystyle |G|_{\text{hits}}} in a sequence of length T {\displaystyle T} , and computes a one-proportion z-test statistic: z = | G | hits − γ T T γ ( 1 − γ ) {\displaystyle z={\frac {|G|_{\text{hits}}-\gamma T}{\sqrt {T\gamma (1-\gamma )}}}} Under the null hypothesis that the text was written by an unwatermarked source (human or another model), the green-hit count is approximately binomially distributed with mean γ T {\displaystyle \gamma T} ; a large positive z {\displaystyle z} rejects the null hypothesis. The original paper reports that fewer than 25 watermarked tokens are sufficient to detect a watermark with a false positive rate below 10^-5 on the OPT-1.3B model. A follow-up study by the same group documented robustness under temperature sampling, top-p (nucleus) sampling, and human paraphrasing, and proposed sliding-window
Soterml
SoTerML (Soil and Terrain Markup Language) is a XML-based markup language for storing and exchanging soil and terrain related data. SoTerML development is being done within The e-SoTer Platform. GEOSS plans a global Earth Observation System and, within this framework, the e-SOTER project addresses the felt need for a global soil and terrain database. The Centre for Geospatial Science (Currently Nottingham Geospatial Institute) at the University of Nottingham has initiated the development since January 2009. Further development and maintenance is currently handled in National Soil Resources Institute (NSRI) at Cranfield University, UK. The role of CGS is within the development of the e-SOTER dissemination platform, which is based on INSPIRE principles. The SoTerML development included: 1. Development of a data dictionary for nomenclatures and various data sources (data and metadata). 2. Development of an exchange format/procedures from the World Reference Base 2006.
CloudPassage
CloudPassage is a company that provides an automation platform, delivered via software as a service, that improves security for private, public, and hybrid cloud computing environments. CloudPassage is headquartered in San Francisco. == History == CloudPassage was founded by Carson Sweet, Talli Somekh, and Vitaliy Geraymovych in 2010. The company used cloud computing and big data analytics to implement security monitoring and control in a platform called Halo. CloudPassage spent a year in stealth developing the Halo technology, coming out of stealth mode to a closed beta in January 2011. In June 2012, the company launched the commercial product that included configuration security monitoring, network microsegmentation, and two-factor authentication for privileged access management. By 2013, CloudPassage expanded Halo to support large enterprises with advanced security and compliance requirements with a product called Halo Enterprise. The first round of venture funding for the company raised $6.5 million. In April 2012, CloudPassage raised $14 million. The financing round was led by Tenaya Capital. In February 2014, CloudPassage announced that it had raised $25.5 million in funding led by Shasta Ventures. In total, the company has invested over $30 million in its technology and raised approximately $88 million in capital. == Product == The CloudPassage platform provides cloud workload security and compliance for systems hosted in public or private cloud infrastructure environments, including hybrid cloud and multi-cloud workload hosting models. The flagship product the company offers is called Halo. Halo secures virtual servers in public, private, and hybrid cloud infrastructures and provides file integrity monitoring (FIM) while also administering firewall automation, vulnerability monitoring, network access control, security event alerting, and assessment. The Halo platform also provides security applications such as privileged access management, software vulnerability scanning, multifactor authentication, and log-based IDS. In December 2013, CloudPassage set up six servers with Microsoft Windows and Linux operating systems and combinations of popular programs and invited hackers to attempt to hack into the servers. The top prize was $5,000 and the winning hacker was a novice that completed the task in four hours. CloudPassage programmed the servers to use basic default security settings to show how vulnerable cloud computing programs can be to security threats. == Awards and recognition == In May 2011, Gigaom named CloudPassage in its list of the Top 50 Cloud Innovators. That same month, eWeek recognized CloudPassage as one of 16 Hot Startup Companies Flying Under the Radar. SC Magazine named CloudPassage an Industry Innovator in the Virtualization and Cloud Security category in 2012. Also in 2012, The Wall Street Journal named CloudPassage a runner-up in the Information Security category of its Technology Innovation Awards. The CloudPassage large-scale security program, Halo, won Best Security Solution in 2014 at the SIIA Codie awards.