Technology is invading in every sector. New inventions, innovation and devices are making life easier for everyone. Voice recognition technology is one such amazing initiative to look for in the growing innovation era. Voice recognition also known as speech recognition, is a computer software program or a hardware device with the ability to receive, interpreting and understanding voice and carry out commands. The technology unravels the feature to easily create and control documents by speaking, with the help of technology.

Of a plethora of digital technologies running around in the web space and an immense part of the transportation industry; AI or artificial intelligence has something significant to offer. It is one such technology which is building concrete impact all over the market place, especially with the truck rental services. Perhaps, there are millions of freight rental providers all over the globe, and more than half are already accustomed to the new end technology. Even the collision of AI and supply chain has given proven results. To know the topic better, it would be a good idea, to begin with, a conversation right now.

Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data. Blends, such as "innoventor", are one particularly challenging class of OOV, as they are formed by fusing together two or more bases that relate to the intended meaning in unpredictable manners and degrees. In this work, we run experiments on a novel dataset of English OOV blends to quantify the difficulty of interpreting the meanings of blends by large-scale contextual language models such as BERT. We first show that BERT's processing of these blends does not fully access the component meanings, leaving their contextual representations semantically impoverished. We find this is mostly due to the loss of characters resulting from blend formation. Then, we assess how easily different models can recognize the structure and recover the origin of blends, and find that context-aware embedding systems outperform character-level and context-free embeddings, although their results are still far from satisfactory.

Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. As one of the most popular graph-based SSL approaches, the recently proposed Graph Convolutional Networks (GCNs) have gained remarkable progress by combining the sound expressiveness of neural networks with graph structure. Nevertheless, the existing graph-based methods do not directly address the core problem of SSL, i.e., the shortage of supervision, and thus their performances are still very limited. To accommodate this issue, a novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure. Firstly, by designing a semi-supervised contrastive loss, improved node representations can be generated via maximizing the agreement between different views of the same data or the data from the same class. Therefore, the rich unlabeled data and the scarce yet valuable labeled data can jointly provide abundant supervision information for learning discriminative node representations, which helps improve the subsequent classification result. Secondly, the underlying determinative relationship between the data features and input graph topology is extracted as supplementary supervision signals for SSL via using a graph generative loss related to the input features. Intensive experimental results on a variety of real-world datasets firmly verify the effectiveness of our algorithm compared with other state-of-the-art methods.

Exam cheating is as ancient a practice as education itself. Back in ancient China, cheating on the Imperial exams was a serious offense. And yet a Qing dynasty cheatsheet--in the shape of a handkerchief with 10,000 symbols in microscopic writing--is on display at the Minneapolis Institute of Arts, showing that students have always been inclined to borrow knowledge and ideas. Exam cheating is as ancient a practice as education itself. Historically, students could attempt to use their own handwriting as false proof of originality, but now the digital age has changed that.

Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief trackers often requires expensive turn-level annotations of every user utterance. In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning. We propose a probabilistic dialog model, called the LAtent BElief State (LABES) model, where belief states are represented as discrete latent variables and jointly modeled with system responses given user inputs. Such latent variable modeling enables us to develop semi-supervised learning under the principled variational learning framework. Furthermore, we introduce LABES-S2S, which is a copy-augmented Seq2Seq model instantiation of LABES. In supervised experiments, LABES-S2S obtains strong results on three benchmark datasets of different scales. In utilizing unlabeled dialog data, semi-supervised LABES-S2S significantly outperforms both supervised-only and semi-supervised baselines. Remarkably, we can reduce the annotation demands to 50% without performance loss on MultiWOZ.

Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream, which can detect unusual group anomalies as they occur, in a dynamic manner. MStream has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MStream is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.

Today, financial mobile applications and similar digital platforms need to provide much more than simple banking processes like payments and transactions, due to consumers' high expectations and an increasingly competitive e-banking industry. FIs already recognize that advanced technologies like artificial intelligence (AI), machine learning (ML), applied robotics, and biometrics are more and more relevant to deliver innovative products and services that cater to consumers' dynamic needs. These ever-changing needs require solid prioritization by FIs in terms of investment and shifting their budgets to the proper channels in hopes of overcoming the competition and delivering what their customers expect. A dramatic shift was shown in global priorities in the banking sector post-COVID-19, pertaining to 2020 and into 2021. The actual digital transformation stood out as a top priority for banks, with 75% of them acknowledging that these dire times are in critical need for them to fully switch their offerings to digital channels.

When San Francisco went into COVID-19 lockdown on March 17, the last thing 32-year-old tech entrepreneur Niket Desai had to worry about was staying fit. His regular spot, Barry's, would be closed indefinitely, but Desai had installed the Tempo Studio, an all-in-one home fitness device designed to turn 30 square feet of your living room into an artificial- intelligence-powered micro gym. Tempo is a six-foot-tall weight cabinet (weights included!) While similar devices, like Tonal, offer digital resistance training at home, Tempo is the first one to deploy 3D movement analysis, combined with machine learning and AI to improve your form and curate your workouts. Its screen streams more than 200 live and on-demand classes, from a ten-minute high-intensity workout to an hour of mobility training, while its motion sensors and AI isolate up to 25 different joints at 30 frames per second.

Growing up in a middle-class family in Kolkata and Midnapore in West Bengal, India, Dr Sayonsom Chanda was no stranger to strong winds and relentless rain knocking down electricity for hours, days, and even weeks. He and his family lived in East Midnapur through the horror of the 1999 Odisha cyclone and Sidr cyclone in 2007. It was one of the core reasons for him to start Sync Energy in 2017. The startup builds artificial intelligence (AI) tools that simplify emergency and disaster response planning for electric power distribution companies. The platform helps electric utilities reduce customer downtimes and be better informed about the impact of a disaster before it actually strikes. This, in-turn, will help electric power companies to decrease costs associated with emergency-related power outages.