Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone currently limited signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new unknown creating a window of opportunity for attackers. As smartphones become host sensitive data applications, extended malware detection mechanisms necessary complying the corresponding resource constraints. The...

Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional often struggle with robustness generalization, particularly dynamic volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve optimize financial metrics. AlphaSharpe generates enhanced risk-return that outperform approaches correlation future employing iterative...

Recent advances in large language models (LLMs) have shown remarkable performance across diverse tasks. However, these are typically deployed with fixed weights, which limits their ability to adapt dynamically the variability inherent real-world data during inference. This paper introduces ChamaleonLLM, a novel framework that enables inference-time adaptation of LLMs by leveraging batch-aware clustering and on-the-fly generation low-rank updates. Unlike traditional fine-tuning approaches...

This paper introduces an advanced methodology for machine translation (MT) corpus generation, integrating semi-automated, human-in-the-loop post-editing with large language models (LLMs) to enhance efficiency and quality. Building upon previous work that utilized real-time training of a custom MT quality estimation metric, this system incorporates novel LLM features such as Enhanced Translation Synthesis Assisted Annotation Analysis, which improve initial hypotheses assessments,...

In an era of rapid technological advancements, agentification software tools has emerged as a critical innovation, enabling systems to function autonomously and adaptively. This paper introduces MediaMind case study demonstrate the process, highlighting how existing can be transformed into intelligent agents capable independent decision-making dynamic interaction. Developed by aiXplain, leverages agent-based architecture monitor, analyze, provide insights from multilingual media content in...

In this work, we present a new technique for efficient use of 3D space around mobile device interaction with the device. Around Device Interaction (ADI) enables extending small and tangible devices beyond their physical boundary. Our proposed method is based on using compass (magnetic field) sensor integrated in (e.g. iPhone 3GS, G1 Android). method, properly shaped permanent magnet shape rod, pen or ring) used interaction. The user makes coarse gestures magnet. Movement affects magnetic...

We present a new technique based on using embedded compass (magnetic) sensor for efficient use of 3D space around mobile device interaction with the device. Around Device Interaction (ADI) enables extending small and tangible devices beyond their physical boundary. Our proposed method is (magnetic field) integrated in (e.g. iPhone 3GS, G1/2 Android). In this method, properly shaped permanent magnet rod, pen or ring) used interaction. The user makes coarse gestures magnet. Movement affects...

In this work, we present a new approach for text (mainly digit) entry based on digit shaped gestures created in 3D space around mobile device. Some devices such as Apple iPhone 3GS and Google Android are equipped with magnetic (compass) sensor. The main idea is to influence the sensor using magnet taken hand. user draws (writes) digits device Movement of changes temporal pattern field which sensed registered by then compared against already recorded templates different digits. Such (digit)...

Playing musical instruments such as chordophones, percussions and keyboard types accompany with harmonic interaction of player's hand the instruments. In this work, we present a novel approach that enables user to imitate music playing gestures around mobile devices. our approach, touch-less gestures, which change magnetic field device, are employed for interaction. The activity an instrument can be transparently pursued by moving tiny magnet in new generation phones equipped embedded...

In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection feature time-series computed from the skeletal representation body in motion. discriminatively optimize forest model over to identify most effective subset features, localized both time and space. Later, train support vector machine classifier selected This approach improves upon baseline...

In the realm of automatic speech recognition (ASR), quest for models that not only perform with high accuracy but also offer transparency in their decision-making processes is crucial. The potential quality estimation (QE) metrics introduced and evaluated as a novel tool to enhance explainable artificial intelligence (XAI) ASR systems. Through experiments analyses, capabilities NoRefER (No Reference Error Rate) metric are explored identifying word-level errors aid post-editors refining...

The common standard for quality evaluation of automatic speech recognition (ASR) systems is reference-based metrics such as the Word Error Rate (WER), computed using manual ground-truth transcriptions that are time-consuming and expensive to obtain. This work proposes a multi-language referenceless metric, which allows comparing performance different ASR models on dataset without ground truth transcriptions. To estimate hypotheses, pre-trained language model (LM) fine-tuned with contrastive...

Digital music performance require high degree of interaction using natural, intuitive input controllers that provide fast feedback on user's action. One the primary considerations professional artists is a powerful and creative tool minimizes number steps required for speed-demanding processes. Most musical applications, which are designed mobile devices, use touch-screen or accelerometer as modalities. In this work, we present novel interface based magnetic field sensor embedded in recent...

In this work, we have propose a novel design for basic mobile phone, which is focused on the essence of communication and connectivity, based silent speech interface auditory feedback. This assistive takes advantages voice control systems while discarding its disadvantages such as background noise, privacy social acceptance. The proposed device utilizes low-cost commercially available hardware components. Thus, it would be affordable accessible by majority users including disabled, elderly...

This paper proposes a novel meta-learning approach to optimize robust portfolio ensemble. The method uses deep generative model generate diverse and high-quality sub-portfolios combined form the ensemble portfolio. consists of convolutional layer, stateful LSTM module, dense network. During training, takes randomly sampled batch Gaussian noise outputs population solutions, which are then evaluated using objective function problem. weights updated gradient-based optimizer. layer transforms...