Online presence on social media platforms such as Facebook and Twitter has become a daily habit for internet users. Despite the vast amount of services the platforms offer for their users, users suffer from cyber-bullying, which further leads to mental abuse and may escalate to cause physical harm to individuals or targeted groups. In this paper, we present our submission to the Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated Arabic Twitter dataset. The shared task consists of 3 sub-tasks, sub-task A focuses on detecting whether the tweet is offensive or not. Then, For offensive Tweets, sub-task B focuses on detecting whether the tweet is hate speech or not. Finally, For hate speech Tweets, sub-task C focuses on detecting the fine-grained type of hate speech among six different classes. Transformer models proved their efficiency in classification tasks, but with the problem of over-fitting when fine-tuned on a small or an imbalanced dataset. We overcome this limitation by investigating multiple training paradigms such as Contrastive learning and Multi-task learning along with Classification fine-tuning and an ensemble of our top 5 performers. Our proposed solution achieved 0.841, 0.817, and 0.476 macro F1-average in sub-tasks A, B, and C respectively.

Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and expertise. The DIGIT tactile sensor offers low-cost entry to high-resolution touch using GelSight-type sensors. Here we customize the DIGIT to have a 3D-printed sensing surface based on the TacTip family of soft biomimetic optical tactile sensors. The DIGIT-TacTip (DigiTac) enables direct comparison between these distinct tactile sensor types. For this comparison, we introduce a tactile robot system comprising a desktop arm, mounts and 3D-printed test objects. We use tactile servo control with a PoseNet deep learning model to compare the DIGIT, DigiTac and TacTip for edge- and surface-following over 3D-shapes. All three sensors performed similarly at pose prediction, but their constructions led to differing performances at servo control, offering guidance for researchers selecting or innovating tactile sensors. All hardware and software for reproducing this study will be openly released. Project website: www.lepora.com/digitac. Project repository: www.github.com/nlepora/digitac-design.

In a multiple-input multiple-output frequency-division duplexing (MIMO-FDD) system, the user equipment (UE) sends the downlink channel state information (CSI) to the base station to report link status. Due to the complexity of MIMO systems, the overhead incurred in sending this information negatively affects the system bandwidth. Although this problem has been widely considered in the literature, prior work generally assumes an ideal feedback channel. In this paper, we introduce PRVNet, a neural network architecture inspired by variational autoencoders (VAE) to compress the CSI matrix before sending it back to the base station under noisy channel conditions. Moreover, we propose a customized loss function that best suits the special characteristics of the problem being addressed. We also introduce an additional regularization hyperparameter for the learning objective, which is crucial for achieving competitive performance. In addition, we provide an efficient way to tune this hyperparameter using KL-annealing. Experimental results show the proposed model outperforms the benchmark models including two deep learning-based models in a noise-free feedback channel assumption. In addition, the proposed model achieves an outstanding performance under different noise levels for additive white Gaussian noise feedback channels.

The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning; this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans.

The Data Science and Machine-Learning Platforms Market report provides information about the Global industry, including valuable facts and figures. This research study explores the Global Market in detail such as industry chain structures, raw material suppliers, with manufacturing The Data Science and Machine-Learning Platforms Sales market examines the primary segments of the scale of the market. This intelligent study provides historical data from 2015 alongside a forecast from 2022 to 2026. With the present market standards revealed, the Data Science and Machine-Learning Platforms market research report has also illustrated the latest strategic developments and patterns of the market players in an unbiased manner. The report serves as a presumptive business document that can help the purchasers in the global market plan their next courses towards the position of the market's future.

Dubai: In the last decade the MENA region has proved to be one of the world's most lucrative fields for the deployment of AI technologies. Realiste, a leading AI developer in the real estate market has finally launched in the Middle East, changing the real-estate and housing landscape using the latest advanced technologies within all operations. Realiste is an AI-based real estate market development company committed to providing users with accurate and timely market value appraisals. The company's mission is to digitize the real estate market of every major city across the world, while offering free access to appraisals and personalized recommendations. Since launching in December 2021 they have established operations in Riyadh, Dubai, London, New York, and Moscow and are planning more expansions in the next year.

Field Programmable Gate Arrays (FPGAs) are an attractive platform to accelerate CPD due to the vast Recent advances in collecting and analyzing large inherent parallelism and energy efficiency FPGAs can datasets have led to the information being naturally offer. Since sparse MTTKRP is memory bound, improving represented as higher-order tensors. Tensor Decomposition the sustained memory bandwidth and latency transforms input tensors to a reduced latent between the compute units on the FPGA and the external space which can then be leveraged to learn salient features DRAM memory can significantly reduce the of the underlying data distribution. Tensor Decomposition MTTKRP compute time. FPGA facilitates near memory has been successfully employed in many computing with custom adaptive hardware due fields, including machine learning, signal processing, to its reconfigurability and large on-chip BlockRAM and network analysis (Mondelli and Montanari, 2019; memory (Xilinx, 2019). It enables the development Cheng et al., 2020; Wen et al., 2020). Canonical of memory controllers and compute units specialized Polyadic Decomposition (CPD) is the most popular for specific data formats; such customization is not means of decomposing a tensor to a low-rank tensor supported on CPU and GPU.

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together - by both aggregating pre-existing datasets and creating new ones - visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of available unlabelled textual data for pretraining, and only weakly by the typological distance of target-source languages. We hope to encourage future research efforts in this area by releasing the benchmark to the community.

This paper presents techniques to display 3D illuminations using Flying Light Specks, FLSs. Each FLS is a miniature (hundreds of micrometers) sized drone with one or more light sources to generate different colors and textures with adjustable brightness. It is network enabled with a processor and local storage. Synchronized swarms of cooperating FLSs render illumination of virtual objects in a pre-specified 3D volume, an FLS display. We present techniques to display both static and motion illuminations. Our display techniques consider the limited flight time of an FLS on a fully charged battery and the duration of time to charge the FLS battery. Moreover, our techniques assume failure of FLSs is the norm rather than an exception. We present a hardware and a software architecture for an FLS-display along with a family of techniques to compute flight paths of FLSs for illuminations. With motion illuminations, one technique (ICF) minimizes the overall distance traveled by the FLSs significantly when compared with the other techniques.

Existing multimodal stress/pain recognition approaches generally extract features from different modalities independently and thus ignore cross-modality correlations. This paper proposes a novel geometric framework for multimodal stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices as a representation that incorporates the correlation relationship of physiological and behavioural signals from covariance and cross-covariance. Considering the non-linearity of the Riemannian manifold of SPD matrices, well-known machine learning techniques are not suited to classify these matrices. Therefore, a tangent space mapping method is adopted to map the derived SPD matrix sequences to the vector sequences in the tangent space where the LSTM-based network can be applied for classification. The proposed framework has been evaluated on two public multimodal datasets, achieving both the state-of-the-art results for stress and pain detection tasks.