Time-series classification is one of the most frequently performed tasks in industrial data science, and one of the most widely used data representation in the industrial setting is tabular representation. In this work, we propose a novel scalable architecture for learning representations from tabular time-series data and subsequently performing downstream tasks such as time-series classification. The representation learning framework is end-to-end, akin to bidirectional encoder representations from transformers (BERT) in language modeling, however, we introduce novel masking technique suitable for pretraining of time-series data. Additionally, we also use one-dimensional convolutions augmented with transformers and explore their effectiveness, since the time-series datasets lend themselves naturally for one-dimensional convolutions. We also propose a novel timestamp embedding technique, which helps in handling both periodic cycles at different time granularity levels, and aperiodic trends present in the time-series data. Our proposed model is end-to-end and can handle both categorical and continuous valued inputs, and does not require any quantization or encoding of continuous features.

Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps. In this article we present a novel method to model radio propagation using deep convolutional neural networks and report significantly improved performance compared to conventional models. We also lay down the framework for data-driven modeling of radio propagation and enable future research to utilize rich and unconventional information of the site, e.g. satellite photos, to provide more accurate and flexible models.

Masayoshi Son has sharply accelerated the pace of his startup investments this year, quintupling the number of companies in his Vision Fund 2 portfolio in less than nine months. The founder of SoftBank Group Corp. has cut 115 deals this year, according to Bloomberg calculations based on data released by the company. That is more than the combined number of deals the first Vision Fund made since its start in 2017, showing Son remains confident in his investing capability despite blunders with office-sharing service WeWork and financier Greensill. The faster pace of deal-making is sure to raise questions about whether Son is risking similar missteps, especially as a string of high-profile departures depletes top talent at the Vision Fund. Seven managing partners have left since March of last year, and last week Deep Nishar, the sole senior managing partner and leading authority on AI, said he would depart by the end of the year.

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multiple iIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in ...

The above quotation becomes the basis of what is present in this article, studying natural language processing in individual personality. Personality is defined as the characteristic set of behaviours, cognitions, and emotional patterns [1] as well as thinking patterns [2], and its external appearance can be seen in writing, speech, decision and other aspects of the social and personal lives of people. Language is the most prominent and the most available aspects of individuals' personality. Meanwhile, written text is one of the most utilized appearance of language. Developing the Internet based infrastructure such as social media, e-mails, and different texting contexts, have made the language appearance of people more available. Consequently, considering the increasing of internet based communications, it would be so exciting to became aware of individuals' personality, inspite of their absence. Therefore, the involvement of computers in determining the personality of people seems necessary and turned into a study field in computer science. Automatic Personality Prediction (or Perception) (APP) is the automatic prediction of the personality of individuals and usually done by computers.

Line of sight satellite systems, unmanned aerial vehicles, high-altitude platforms, and microwave links that operate on frequency bands such as Ka-band or higher are extremely susceptible to rain. Thus, rain fade forecasting for these systems is critical because it allows the system to switch between ground gateways proactively before a rain fade event to maintain seamless service. Although empirical, statistical, and fade slope models can predict rain fade to some extent, they typically require statistical measurements of rain characteristics in a given area and cannot be generalized to a large scale system. Furthermore, such models typically predict near-future rain fade events but are incapable of forecasting far into the future, making proactive resource management more difficult. In this paper, a deep learning (DL)-based architecture is proposed that forecasts future rain fade using satellite and radar imagery data as well as link power measurements. Furthermore, the data preprocessing and architectural design have been thoroughly explained and multiple experiments have been conducted. Experiments show that the proposed DL architecture outperforms current state-of-the-art machine learning-based algorithms in rain fade forecasting in the near and long term. Moreover, the results indicate that radar data with weather condition information is more effective for short-term prediction, while satellite data with cloud movement information is more effective for long-term predictions.

In today's world, technology advances at an extremely fast pace, with new innovations and advancements made every day. Things change quickly in this industry so it's vital to keep up with the latest IT and telecommunications technology trends. Cognitive technology is a very exciting area of technology that we have already seen huge breakthroughs in, and facial recognition is a clear example of this. The developments being made in cognitive technology today will lead to drastic changes in humanity's relationship with machines and their understanding of humans. Today, we see these technologies commonly used in virtual assistants and smart speakers but there is huge potential for them to have a huge impact on a wide range of industries and sectors.

Communication Service Providers (CSPs) are making AI deployments an immediate priority to improve service experience for customers and reduce operational costs, an Anodot survey reveals. Furthermore, 53% of respondents stated that improving service experience was the primary driver for implementing AI-based network monitoring and detection. These findings are based on an independent Heavy Reading global survey of nearly 100 senior networking and IT CSP decision makers in Q3 2021. "Instead of waiting for next generation 5G network deployments to invest in AI, the majority of CSPs are already deploying AI on 4G networks now, the infrastructure most of their customers still use," said Anodot CEO David Drai. "With AI-based network monitoring, CSPs can detect network issues up to 80% faster and reduce incident costs by as much as 70%."

Fully Connected Neural Network (FCNN) is a class of Artificial Neural Networks widely used in computer science and engineering, whereas the training process can take a long time with large datasets in existing many-core systems. Optical Network-on-Chip (ONoC), an emerging chip-scale optical interconnection technology, has great potential to accelerate the training of FCNN with low transmission delay, low power consumption, and high throughput. However, existing methods based on Electrical Network-on-Chip (ENoC) cannot fit in ONoC because of the unique properties of ONoC. In this paper, we propose a fine-grained parallel computing model for accelerating FCNN training on ONoC and derive the optimal number of cores for each execution stage with the objective of minimizing the total amount of time to complete one epoch of FCNN training. To allocate the optimal number of cores for each execution stage, we present three mapping strategies and compare their advantages and disadvantages in terms of hotspot level, memory requirement, and state transitions. Simulation results show that the average prediction error for the optimal number of cores in NN benchmarks is within 2.3%. We further carry out extensive simulations which demonstrate that FCNN training time can be reduced by 22.28% and 4.91% on average using our proposed scheme, compared with traditional parallel computing methods that either allocate a fixed number of cores or allocate as many cores as possible, respectively. Compared with ENoC, simulation results show that under batch sizes of 64 and 128, on average ONoC can achieve 21.02% and 12.95% on reducing training time with 47.85% and 39.27% on saving energy, respectively.

In this work, we propose a Deep neural network-assisted Particle Filter-based (DePF) approach to address the Mobile User (MU) joint synchronization and localization (sync\&loc) problem in ultra dense networks. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the Access Points (APs), which, traditionally, provides us with information about the MUs' clock offset and skew. However, information about the distance between an AP and an MU is also intrinsic to the propagation delay experienced by exchanged time-stamps. In addition, to estimate the angle of arrival of the received synchronization packet, DePF draws on the multiple signal classification algorithm that is fed by Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS. Finally, to perform joint sync\&loc, DePF capitalizes on particle Gaussian mixtures that allow for a hybrid particle-based and parametric Bayesian Recursive Filtering (BRF) fusion of the aforementioned pieces of information and thus jointly estimate the position and clock parameters of the MUs. The simulation results verifies the superiority of the proposed algorithm over the state-of-the-art schemes, especially that of Extended Kalman filter- and linearized BRF-based joint sync\&loc. In particular, only drawing on the synchronization time-stamp exchange and CIRs, for 90$\%$of the cases, the absolute position and clock offset estimation error remain below 1 meter and 2 nanoseconds, respectively.