Reviewerly/exHarmony huggingface dataset:
Hybrid and Oriented Harmonic Potentials for Safe Task Execution in Unknown Environment

Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation policies. However, for complex environments that consist of a large number of overlapping non-sphere obstacles, the computation of associated transformation functions can be tedious. This becomes more apparent when: (i) the workspace is initially unknown and the underlying potential fields are updated constantly as the robot explores it; (ii) the high-level mission consists of sequential navigation tasks among numerous regions, requiring the robot to switch between different potentials. Thus, this work proposes an efficient and automated scheme to construct harmonic potentials incrementally online as guided by the task automaton. A novel two-layer harmonic tree (HT) structure is introduced that facilitates the hybrid combination of oriented search algorithms for task planning and harmonic-based navigation controllers for non-holonomic robots. Both layers are adapted efficiently and jointly during online execution to reflect the actual feasibility and cost of navigation within the updated workspace. Global safety and convergence are ensured both for the high-level task plan and the low-level robot trajectory. Known issues such as oscillation or long-detours for purely potential-based methods and sharp-turns or high computation complexity for purely search-based methods are prevented. Extensive numerical simulation and hardware experiments are conducted against several strong baselines.
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models

Modern image-to-text systems typically adopt the encoder-decoder framework, which comprises two main components: an image encoder, responsible for extracting image features, and a transformer-based decoder, used for generating captions. Taking inspiration from the analysis of neural networks' robustness against adversarial perturbations, we propose a novel gray-box algorithm for creating adversarial examples in image-to-text models. Unlike image classification tasks that have a finite set of class labels, finding visually similar adversarial examples in an image-to-text task poses greater challenges because the captioning system allows for a virtually infinite space of possible captions. In this paper, we present a gray-box adversarial attack on image-to-text, both untargeted and targeted. We formulate the process of discovering adversarial perturbations as an optimization problem that uses only the image-encoder component, meaning the proposed attack is language-model agnostic. Through experiments conducted on the ViT-GPT2 model, which is the most-used image-to-text model in Hugging Face, and the Flickr30k dataset, we demonstrate that our proposed attack successfully generates visually similar adversarial examples, both with untargeted and targeted captions. Notably, our attack operates in a gray-box manner, requiring no knowledge about the decoder module. We also show that our attacks fool the popular open-source platform Hugging Face.
Soft Language Clustering for Multilingual Model Pre-training

Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME including text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer.
Is Anisotropy Inherent to Transformers?

The representation degeneration problem is a phenomenon that is widely observed among self-supervised learning methods based on Transformers. In NLP, it takes the form of anisotropy, a singular property of hidden representations which makes them unexpectedly close to each other in terms of angular distance (cosine-similarity). Some recent works tend to show that anisotropy is a consequence of optimizing the cross-entropy loss on long-tailed distributions of tokens. We show in this paper that anisotropy can also be observed empirically in language models with specific objectives that should not suffer directly from the same consequences. We also show that the anisotropy problem extends to Transformers trained on other modalities. Our observations tend to demonstrate that anisotropy might actually be inherent to Transformers-based models.
Hybrid lemmatization in HuSpaCy

Lemmatization is still not a trivial task for morphologically rich languages. Previous studies showed that hybrid architectures usually work better for these languages and can yield great results. This paper presents a hybrid lemmatizer utilizing both a neural model, dictionaries and hand-crafted rules. We introduce a hybrid architecture along with empirical results on a widely used Hungarian dataset. The presented methods are published as three HuSpaCy models.
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to generate the most suitable style for the text without requiring reference speech, achieving efficient latent diffusion while benefiting from the diverse speech synthesis offered by diffusion models. Furthermore, we employ large pre-trained SLMs, such as WavLM, as discriminators with our novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. Moreover, when trained on the LibriTTS dataset, our model outperforms previous publicly available models for zero-shot speaker adaptation. This work achieves the first human-level TTS on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs. The audio demos and source code are available at https://styletts2.github.io/.
Provably Learning Nash Policies in Constrained Markov Potential Games

Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective. In many real-world instances, the agents may not only want to optimize their objectives, but also ensure safe behavior. For example, in traffic routing, each car (agent) aims to reach its destination quickly (objective) while avoiding collisions (safety). Constrained Markov Games (CMGs) are a natural formalism for safe MARL problems, though generally intractable. In this work, we introduce and study Constrained Markov Potential Games (CMPGs), an important class of CMGs. We first show that a Nash policy for CMPGs can be found via constrained optimization. One tempting approach is to solve it by Lagrangian-based primal-dual methods. As we show, in contrast to the single-agent setting, however, CMPGs do not satisfy strong duality, rendering such approaches inapplicable and potentially unsafe. To solve the CMPG problem, we propose our algorithm Coordinate-Ascent for CMPGs (CA-CMPG), which provably converges to a Nash policy in tabular, finite-horizon CMPGs. Furthermore, we provide the first sample complexity bounds for learning Nash policies in unknown CMPGs, and, which under additional assumptions, guarantee safe exploration.
V-LoL: A Diagnostic Dataset for Visual Logical Learning

Despite the successes of recent developments in visual AI, different shortcomings still exist; from missing exact logical reasoning, to abstract generalization abilities, to understanding complex and noisy scenes. Unfortunately, existing benchmarks, were not designed to capture more than a few of these aspects. Whereas deep learning datasets focus on visually complex data but simple visual reasoning tasks, inductive logic datasets involve complex logical learning tasks, however, lack the visual component. To address this, we propose the visual logical learning dataset, V-LoL, that seamlessly combines visual and logical challenges. Notably, we introduce the first instantiation of V-LoL, V-LoL-Trains, -- a visual rendition of a classic benchmark in symbolic AI, the Michalski train problem. By incorporating intricate visual scenes and flexible logical reasoning tasks within a versatile framework, V-LoL-Trains provides a platform for investigating a wide range of visual logical learning challenges. We evaluate a variety of AI systems including traditional symbolic AI, neural AI, as well as neuro-symbolic AI. Our evaluations demonstrate that even state-of-the-art AI faces difficulties in dealing with visual logical learning challenges, highlighting unique advantages and limitations specific to each methodology. Overall, V-LoL opens up new avenues for understanding and enhancing current abilities in visual logical learning for AI systems.
Effect of Malaria on Hematological Profile in Medical Unit-2 of Chandka Medical College Larkana

Objective: To see effects of malaria on haematological profile and to see the difference of changes due to malaria on hematological profile in both males and females. Study Design: Case control Place and Duration of Study: Medical unit-2 Department of Chandka Medical College, Larkana from 1st June 2022 to 30th November 2022. Methodology: Eighty seven patients of malaria and 80 without malaria were enrolled. A 3% Giemsa staining technique was applied for processing and staining of the films. Parasite density /ml blood was assessed. The blood samples taken from all participants were analyzed through proper quality assurances. The hematological profile included white blood cell counts (WBC), hemoglobin measurements (Hb), percentage of monocytes (MON%), percentage of lymphocytes (LYM%), percentage of neutrophils (NEUT%) as well as red blood cell distribution width (RDW-CV), and also the platelet counts (PLT). Results: Malaria had an impact on the hemoglobin levels. Among our patients, 73.5% showed mild anemia, 9.1% had moderate anemia, and 14.9% were diagnosed with severe anemia. Females were found to be more affected than males. The sensitivity and specificity of the study was estimated for all the hematological profiling and it was found that White blood cells, monocytes as well as hemoglobulin were very sensitive and specific predictors for malaria presence. Conclusion: Hemoglobulin levels, white blood cells count as well as platelets count are decreased significantly in malarial cases. Keywords: Malaria, Anemia, Thrombocytopenia, Hematological profile, Rapid acting ICT method, Plasmodium
Adoption and use of electronic-mail by staff in Federal College of Education (Technical), Gombe

The purpose of this study is to examine the adoption and use of electronic-mail by staff in Federal College of Education (Technical) Gombe. The objectives of the study include: to assess the level of usage of e-mail among staff of Federal College of Education, Gombe, to determine the impacts of adoption of e-mail among staff in the College and to identify the challenges associated with the use osf e-mail among the staff of the College under study. This study was guided by descriptive research design. The population of this study consisted of 1449 academic and nonacademic staff across various departments and units in the College. Simple random sampling in probability sampling techniques was used for this study and the sample size of the study was 302. The findings of the study revealed that respondents used email to share information faster than the conventional mail. The study also revealed that the adoption of email enhanced efficiency and promotes productivity in service delivery among the staff. The study concluded that though not all the public higher institutions in Gombe Staff participated in this study, the use of email were found to be useful among the staff. The study recommended that the adoption and use of e-mail should be given priority as a means of communication to enhance productivity in the delivery of information. Also, there is also the need to facilitate easy access to information as a means of enhancing impacts to improve public information system among the staff.
The use of data mining and data protection technology in the 21st Century library

Technological development has brought dramatic changes in the management, retrieving and dissemination of information. Information technology has influenced positively on library and information systems, services and protection of library resources for users. Mining of hidden and advance knowledge that is potentially useful including safeguarding these information resources that has been mined from compromise or loss cannot be overemphasize. This study enumerates various data mining and data protection technologies use in the 21st Century library including the benefit of using these technologies in the library. However, the study illustrated the challenges encounter in the use of these technologies to include computer network support, using a variety of data mining, deeply mining, coordination of information management personnel and finally centering on readers and timely processing feedback information. The study conclude that data mining and data protection technologies are cross-discipline, and are one of the most cutting-edge researches in the international database and information system, so its use in the 21st Century library is still in its infancy and that the learning and use of these technologies should be strengthen since it is a new trend in librarianship.
Logic and computation as combinatorics

The syntactic nature of logic and computation separates them from other fields of mathematics. Nevertheless, syntax has been the only way to adequately capture the dynamics of proofs and programs such as cut-elimination, and the finiteness and the atomicity of syntax are preferable for foundational aims as seen in Hilbert's program. Another issue is that a uniform basis for logic and computation has been missing, and this problem hampers a coherent view on them. For instance, formal proofs in proof theory are far from (ordinary) proofs of the validity of a formula in model theory. Our goal is to solve these fundamental problems by rebuilding central concepts in logic and computation such as formal systems, validity (in such a way that it coincides with the existence of proofs), cut-elimination and computability uniformly in terms of finite graphs based on game semantics. Unlike game semantics, however, we do not rely on anything infinite or extrinsic to the graphs. A key idea that enables our finitary, autonomous approach is the shift from graphs in game semantics to dynamic ones. The resulting combinatorics establishes a single, syntax-free, finitary framework that recasts formal systems admitting proofs with cuts, validity, the finest computational steps of cut-elimination and higher-order computability. This subsumes fully complete semantics of intuitionistic linear logic, which solves a problem open for thirty years, and even extends the full completeness to proofs with cuts. As a byproduct, our dynamic graphs give rise to Hopf algebras, which opens up new applications of algebras to logic and computation.
New Optimal Results on Codes for Location in Graphs

In this paper, we broaden the understanding of the recently introduced concepts of solid-locating-dominating and self-locating-dominating codes in various graphs. In particular, we present the optimal, i.e., smallest possible, codes in the infinite triangular and king grids. Furthermore, we give optimal locating-dominating, self-locating-dominating and solid-locating-dominating codes in the direct product $K_n\times K_m$ of complete graphs. We also present optimal solid-locating-dominating codes for the Hamming graphs $K_q\square K_q\square K_q$ with $q\geq2$.
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity

Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. Current pipelines either use an object detector together with a pose estimator (top-down approach), or localize all body parts first and then link them to predict the pose of individuals (bottom-up). Yet, when individuals closely interact, top-down methods are ill-defined due to overlapping individuals, and bottom-up methods often falsely infer connections to distant bodyparts. Thus, we propose a novel pipeline called bottom-up conditioned top-down pose estimation (BUCTD) that combines the strengths of bottom-up and top-down methods. Specifically, we propose to use a bottom-up model as the detector, which in addition to an estimated bounding box provides a pose proposal that is fed as condition to an attention-based top-down model. We demonstrate the performance and efficiency of our approach on animal and human pose estimation benchmarks. On CrowdPose and OCHuman, we outperform previous state-of-the-art models by a significant margin. We achieve 78.5 AP on CrowdPose and 48.5 AP on OCHuman, an improvement of 8.6% and 7.8% over the prior art, respectively. Furthermore, we show that our method strongly improves the performance on multi-animal benchmarks involving fish and monkeys. The code is available at https://github.com/amathislab/BUCTD
FOOCTTS: Generating Arabic Speech with Acoustic Environment for Football Commentator

This paper presents FOOCTTS, an automatic pipeline for a football commentator that generates speech with background crowd noise. The application gets the text from the user, applies text pre-processing such as vowelization, followed by the commentator's speech synthesizer. Our pipeline included Arabic automatic speech recognition for data labeling, CTC segmentation, transcription vowelization to match speech, and fine-tuning the TTS. Our system is capable of generating speech with its acoustic environment within limited 15 minutes of football commentator recording. Our prototype is generalizable and can be easily applied to different domains and languages.
Understanding Telecom Language Through Large Language Models

The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and deployment. This has been further pushed forward with the evolution of generative artificial intelligence (AI), including the emergence of large language models (LLMs), which is believed to be the cornerstone toward realizing self-governed, interactive AI agents. Motivated by this, in this paper, we aim to adapt the paradigm of LLMs to the Telecom domain. In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups. We consider training the selected models on 3GPP technical documents (Tdoc) pertinent to years 2009-2019 and predict the Tdoc categories in years 2020-2023. The results demonstrate that fine-tuning BERT and RoBERTa model achieves 84.6% accuracy, while GPT-2 model achieves 83% in identifying 3GPP working groups. The distilled BERT model with around 50% less parameters achieves similar performance as others. This corroborates that fine-tuning pretrained LLM can effectively identify the categories of Telecom language. The developed framework shows a stepping stone towards realizing intent-driven and self-evolving wireless networks from Telecom languages, and paves the way for the implementation of generative AI in the Telecom domain.
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition

End-to-end (E2E) systems have shown comparable performance to hybrid systems for automatic speech recognition (ASR). Word timings, as a by-product of ASR, are essential in many applications, especially for subtitling and computer-aided pronunciation training. In this paper, we improve the frame-level classifier for word timings in E2E system by introducing label priors in connectionist temporal classification (CTC) loss, which is adopted from prior works, and combining low-level Mel-scale filter banks with high-level ASR encoder output as input feature. On the internal Chinese corpus, the proposed method achieves 95.68%/94.18% compared to the hybrid system 93.0%/90.22% on the word timing accuracy metrics. It also surpass a previous E2E approach with an absolute increase of 4.80%/8.02% on the metrics on 7 languages. In addition, we further improve word timing accuracy by delaying CTC peaks with frame-wise knowledge distillation, though only experimenting on LibriSpeech.
THE SYMBOLOLOGY OF THE CONCEPTS "THE BODY WITHOUT ORGANS" BY ANTONEN ARTO AND THE "SUPERMARIONETTE" BY GORDON CRAG AS A PHENOMENON OF METANTHROPOLOGY OF STAGE SYNTHESIS

The purpose of the article is to define the philosophical and anthropological boundaries of the stage space as a game phenomenon. The research methodology is focused on comparative and systemic approaches, which enables a comparative analysis of the formation of the integrity of the synergistic, architectural, plastic dimensions of the artistic image of the scenic synthesis of arts. Scientific novelty of the article. It is proved that the "theatre of cruelty", according to A. Artaud, is defined in the context of bodily implications, and the "super marionette" of H. Craig is presented as a willful impulse of the cultural and historical resource of the stage play. Conclusions. Concepts-metaphors of A. Artaud and H. Craig influenced the artistic thinking of stage masters, in particular, they found expression in the work of I. Stravinsky, Vs. Meyerhold, B. Brecht.
Implementasi Metode K-Nearest Neighbors (KNN) Guna Mengetahui Klasifikasi Kematangan Stroberi

To determine the maturity of the Strawberry fruit can be seen in the color of the fruit. The color of ripe strawberries can be seen in red and those that are not yet ripe are green. To determine the ripeness of strawberries, classification can be carried out on the fruit using feature extraction of the waena. The feature extraction results are classified using the K-Nearest Neighbors (KNN) method. The first method of classification of strawberry ripeness is (1) feature extraction using the Hue Saturation and Value (HSV) method, and (2) KNN. From the implementation results, the success rate of classification using the KNN method is 76%