Miracle-dz/UniFAR-FTUs huggingface dataset:
Sinhala-English Word Embedding Alignment: Introducing Datasets and Benchmark for a Low Resource Language

Since their inception, embeddings have become a primary ingredient in many flavours of Natural Language Processing (NLP) tasks supplanting earlier types of representation. Even though multilingual embeddings have been used for the increasing number of multilingual tasks, due to the scarcity of parallel training data, low-resource languages such as Sinhala, tend to focus more on monolingual embeddings. Then when it comes to the aforementioned multi-lingual tasks, it is challenging to utilize these monolingual embeddings given that even if the embedding spaces have a similar geometric arrangement due to an identical training process, the embeddings of the languages considered are not aligned. This is solved by the embedding alignment task. Even in this, high-resource language pairs are in the limelight while low-resource languages such as Sinhala which is in dire need of help seem to have fallen by the wayside. In this paper, we try to align Sinhala and English word embedding spaces based on available alignment techniques and introduce a benchmark for Sinhala language embedding alignment. In addition to that, to facilitate the supervised alignment, as an intermediate task, we also introduce Sinhala-English alignment datasets. These datasets serve as our anchor datasets for supervised word embedding alignment. Even though we do not obtain results comparable to the high-resource languages such as French, German, or Chinese, we believe our work lays the groundwork for more specialized alignment between English and Sinhala embeddings.
The decrease in summer surface water temperature with altitude in Swiss Alpine lakes: a comparison with air temperature lapse rates

Using miniature thermistors with integrated data loggers, the decrease in summer lake surface water temperature (LSWT) with increasing altitude a.s. l. was investigated in 10 Swiss Alpine lakes located between 613 m a.s. l. and 2339 m a.s. l. The LSWTs exhibit essentially the same short-term structure as regional air temperature, but are about 3 to 5°C higher than the air temperature at the altitude of the lake. LSWTs decrease approximately linearly with increasing altitude at a rate slightly greater than the surface air temperature lapse rate. Diel variations in LSWT are large, implying that single water temperature measurements are un- likely to be representative of the mean. Local factors will affect LSWT more than they affect air temperature, possibly resulting in severe distortion of the empirical relationship between the two. Several implications for paleoclimate reconstruction studies result. (1) Paleolimnologically reconstructed LSWTs are likely to be higher than the air temperatures prevailing at the altitude of the lake. (2) Lakes used for paleoclimate reconstruction should be selected to minimize local effects on LSWT. (3) The calibration of organism-specific quantitative paleotemperature inference models should not be based on single water temperature measurements. (4) Consideration should be given to calibrating such models directly against air temperature rather than water temperature. (5) The primary climate effect on the aquatic biota of high-altitude lakes may be mediated by the timing of the ice cover.
Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective

The performance of state-of-the-art machine learning models often deteriorates when testing on demographics that are under-represented in the training dataset. This problem has predominately been studied in a supervised learning setting where the data distribution is static. However, real-world applications often involve distribution shifts caused by the deployed models. For instance, the performance disparity against monitory users can lead to a high customer churn rate, thus the available data provided by active users are skewed due to the lack of minority users. This feedback effect further exacerbates the disparity among different demographic groups in future steps. To address this issue, we propose asymptotically fair participation as a condition to maintain long-term model performance over all demographic groups. In this work, we aim to address the problem of achieving asymptotically fair participation via optimal control formulation. Moreover, we design a surrogate retention system based on existing literature on evolutionary population dynamics to approximate the dynamics of distribution shifts on active user counts, from which the objective of achieving asymptotically fair participation is formulated as an optimal control problem, and the control variables are considered as the model parameters. We apply an efficient implementation of Pontryagin's maximum principle to estimate the optimal control solution. To evaluate the effectiveness of the proposed method, we design a generic simulation environment that simulates the population dynamics of the feedback effect between user retention and model performance. When we deploy the resulting models to the simulation environment, the optimal control solution accounts for long-term planning and leads to superior performance compared with existing baseline methods.
You Cannot Escape Me: Detecting Evasions of SIEM Rules in Enterprise Networks

Cyberattacks have grown into a major risk for organizations, with common consequences being data theft, sabotage, and extortion. Since preventive measures do not suffice to repel attacks, timely detection of successful intruders is crucial to stop them from reaching their final goals. For this purpose, many organizations utilize Security Information and Event Management (SIEM) systems to centrally collect security-related events and scan them for attack indicators using expert-written detection rules. However, as we show by analyzing a set of widespread SIEM detection rules, adversaries can evade almost half of them easily, allowing them to perform common malicious actions within an enterprise network without being detected. To remedy these critical detection blind spots, we propose the idea of adaptive misuse detection, which utilizes machine learning to compare incoming events to SIEM rules on the one hand and known-benign events on the other hand to discover successful evasions. Based on this idea, we present AMIDES, an open-source proof-of-concept adaptive misuse detection system. Using four weeks of SIEM events from a large enterprise network and more than 500 hand-crafted evasions, we show that AMIDES successfully detects a majority of these evasions without any false alerts. In addition, AMIDES eases alert analysis by assessing which rules were evaded. Its computational efficiency qualifies AMIDES for real-world operation and hence enables organizations to significantly reduce detection blind spots with moderate effort.
Hashing it Out: Predicting Unhealthy Conversations on Twitter

Personal attacks in the context of social media conversations often lead to fast-paced derailment, leading to even more harmful exchanges being made. State-of-the-art systems for the detection of such conversational derailment often make use of deep learning approaches for prediction purposes. In this paper, we show that an Attention-based BERT architecture, pre-trained on a large Twitter corpus and fine-tuned on our task, is efficient and effective in making such predictions. This model shows clear advantages in performance to the existing LSTM model we use as a baseline. Additionally, we show that this impressive performance can be attained through fine-tuning on a relatively small, novel dataset, particularly after mitigating overfitting issues through synthetic oversampling techniques. By introducing the first transformer based model for forecasting conversational events on Twitter, this work lays the foundation for a practical tool to encourage better interactions on one of the most ubiquitous social media platforms.
Improving Unimodal Inference with Multimodal Transformers

This paper proposes an approach for improving performance of unimodal models with multimodal training. Our approach involves a multi-branch architecture that incorporates unimodal models with a multimodal transformer-based branch. By co-training these branches, the stronger multimodal branch can transfer its knowledge to the weaker unimodal branches through a multi-task objective, thereby improving the performance of the resulting unimodal models. We evaluate our approach on tasks of dynamic hand gesture recognition based on RGB and Depth, audiovisual emotion recognition based on speech and facial video, and audio-video-text based sentiment analysis. Our approach outperforms the conventionally trained unimodal counterparts. Interestingly, we also observe that optimization of the unimodal branches improves the multimodal branch, compared to a similar multimodal model trained from scratch.
UA-Radar: Exploring the Impact of User Agents on the Web

\beginabstract In the early days of the web, giving the same web page to different browsers could provide very different results. As the rendering engine behind each browser would differ, some elements of a page could break or be positioned in the wrong location. At that time, the User Agent (UA) string was introduced for content negotiation. By knowing the browser used to connect to the server, a developer could provide a web page that was tailored for that specific browser to remove any usability problems. Over the past three decades, the UA string remained exposed by browsers, but its current usefulness is being debated. Browsers now adopt the exact same standards and use the same languages to display the same content to users, bringing the question if the content of the UA string is still relevant today, or if it is a relic of the past. Moreover, the diversity of means to browse the web has become so large that the UA string is one of the top contributors to tracking users in the field of browser fingerprinting, bringing a sense of urgency to deprecate it. In this paper, our goal is to understand the impact of the UA on the web and if this legacy string is still actively used to adapt the content served to users. We introduce \radar, a web page similarity measurement tool that compares in-depth two web pages from the code to their actual rendering, and highlights the similarities it finds. We crawled 270,048 web pages from 11,252 domains using 3 different browsers and 2 different UA strings to observe that 100% of the web pages were similar before any JavaScript was executed, demonstrating the absence of differential serving. % Our experiments also show that only a very small number of websites are affected by the lack of UA information, which can be fixed in most cases by updating code to become browser-agnostic. Our study brings some proof that it may be time to turn the page on the UA string and retire it from current web browsers. \endabstract
Growth-rate distributions of gut microbiota time series: neutral models and temporal dependence

Logarithmic growth-rates are fundamental observables for describing ecological systems and the characterization of their distributions with analytical techniques can greatly improve their comprehension. Here a neutral model based on a stochastic differential equation with demographic noise, which presents a closed form for these distributions, is used to describe the population dynamics of microbiota. Results show that this model can successfully reproduce the log-growth rate distribution of the considered abundance time-series. More significantly, it predicts its temporal dependence, by reproducing its kurtosis evolution when the time lag $\tau$ is increased. Furthermore, its typical shape for large $\tau$ is assessed, verifying that the distribution variance does not diverge with $\tau$. The simulated processes generated by the calibrated stochastic equation and the analysis of each time-series, taken one by one, provided additional support for our approach. Alternatively, we tried to describe our dataset by using a logistic model with an environmental stochastic term. Analytical and numerical results show that this model is not suited for describing the leptokurtic log-growth rates distribution found in our data. These results effectively support a neutral model with demographic stochasticity for describing the growth-rate dynamics and the stationary abundance distribution of the considered microbiota. This suggests that there are no significant parametric demographic differences among the species, which can be statistically characterized by the same vital rates.
A Tale of Unrealized Hope: Hardware Performance Counter Against Cache Attacks

This paper investigates an emerging cache side channel attack defense approach involving the use of hardware performance counters (HPCs). These counters monitor microarchitectural events and analyze statistical deviations to differentiate between malicious and benign software. With numerous proposals and promising reported results, we seek to investigate whether published HPC-based detection methods are evaluated in a proper setting and under the right assumptions, such that their quality can be ensured for real-word deployment against cache side-channel attacks. To achieve this goal, this paper presents a comprehensive evaluation and scrutiny of existing literature on the subject matter in a form of a survey, accompanied by experimental evidences to support our evaluation.
Understanding Northern Hemisphere land precipitation change

Water is key to life on Earth. The distribution and quantity of precipitation controls the availability of water, yet little is known about past changes in precipitation. This is especially true at regional scales. The land region of the Northern Hemisphere, especially the mid-latitude region, offers an ideal opportunity to compare and contrast output from climate models with our longest and most comprehensive precipitation observations. This thesis develops current understanding, with the aid of climate models, to attribute changes in global mean precipitation to known key forcings. Perhaps the most obvious feature of twentieth-century global mean precipitation change is a decrease in response to mid-twentieth-century aerosol forcing. Changes in historical precipitation for the land mean of two regions in the Northern Hemisphere are shown to strongly resemble changes in the global mean, due to a greater sensitivity to aerosol forcing than greenhouse gas forcing. This aerosol response is predictable across models, which offer an ideal resource to test this constraint due to their large range in aerosol forcing. However, this aerosol response is not evident in these key midlatitude precipitation observations. Observed runoff changes, derived from river discharge measurements, also contrast with observed precipitation changes in this region. This contrast is a consequence of an obvious breakpoint in the runoffprecipitation relationship. An ensemble of land surface models that are driven with observed precipitation data fails to simulate this contrast and breakpoint. Combined, these two lines of evidence strongly suggest that Northern Hemisphere mid-latitude precipitation observations are unreliable, at least in the early twentieth century. It is expected that the true trend is disguised by inhomogeneities. This should be recognised in future research that is reliant on these data.
Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy

This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms, aiming to assess the performance enhancements and computational implications across a spectrum of models. We explore various classical-to-quantum mapping methods, ranging from basis encoding, angle encoding to amplitude encoding for encoding classical data, we conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding in enhancing classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research directions may involve refining quantum encoding processes to optimize computational efficiency and exploring scalability for real-world applications. Our work contributes to the growing body of knowledge at the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.
Dimensional Crossover in a Quantum Gas of Light

The dimensionality of a system profoundly influences its physical behaviour, leading to the emergence of different states of matter in many-body quantum systems. In lower dimensions, fluctuations increase and lead to the suppression of long-range order. For example, in bosonic gases, Bose-Einstein condensation (BEC) in one dimension requires stronger confinement than in two dimensions. We experimentally study the properties of a harmonically trapped photon gas undergoing Bose-Einstein condensation along the dimensional crossover from one to two dimensions. The photons are trapped in a dye microcavity where polymer nanostructures provide the trapping potential for the photon gas. By varying the aspect ratio of the harmonic trap, we tune from an isotropic two-dimensional confinement to an anisotropic, highly elongated one-dimensional trapping potential. Along this transition we determine the caloric properties of the photon gas and find a softening of the second-order Bose-Einstein condensation phase transition observed in two dimensions to a crossover behaviour in one dimension.
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways, (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97.75\% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 4\%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.
Microlensing of halo objects in the exterior part of the Galaxy

In the context of this paper, microlenses present as oblate clusters of dark matter structures called massive astrophysical compact halo object(MACHO) in the galactic halo are considered. The NFW density profile [1] is derived from the observational data and works best in the halo region of the exterior part of the galaxy. Hence this profile is used to plot the potential, deflection angle, and critical and caustic curves for the aforementioned microlenses using numerical methods. Moreover, this model is compared with an older density profile model [2], and the differences in their caustic and critical curves are pointed out. This leads to the conclusion that the NFW model produces caustic and critical curves that occupy a smaller region than portrayed by the caustic and critical curves produced by the older density profile model. However, the differences are not that significant to these structures act as microlenses as they are so small and beyond the scope of modern telescopes, and thus only the light curves can be detected from these structures.
ReuseSense: With Great Reuse Comes Greater Efficiency; Effectively Employing Computation Reuse on General-Purpose CPUs

Deep Neural Networks (DNNs) are the de facto algorithm for tackling cognitive tasks in real-world applications such as speech recognition and natural language processing. DNN inference comprises numerous dot product operations between inputs and weights that require numerous multiplications and memory accesses, which hinder their performance and energy consumption when evaluated in modern CPUs. In this work, we leverage the high degree of similarity between consecutive inputs in different DNN layers to improve the performance and energy efficiency of DNN inference on CPUs. To this end, we propose ReuseSense, a new hardware scheme that includes ReuseSensor, an engine to efficiently generate the compute and load instructions needed to evaluate a DNN layer accordingly when sensing similar inputs. By intelligently reusing previously computed product values, ReuseSense allows bypassing computations when encountering input values identical to previous ones. Additionally, it efficiently avoids redundant loads by skipping weight loads associated with the bypassed dot product computations. Our experiments show that ReuseSense achieves an 8x speedup in performance and a 74% reduction in total energy consumption across several DNNs on average over the baseline.