The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly. However, the speed of IKM is slower than KM. My thesis proposes two algorithms to speed up IKM while remaining the quality of its clustering result approximately. The first algorithm, called Divisive K-means, improves the speed of IKM by speeding up its splitting process of clusters. Testing with UCI Machine Learning data sets, the new algorithm achieves the empirically global optimum as IKM and has lower complexity, $O(k*log_{2}k*n)$, than IKM, $O(k^{2}n)$. The second algorithm, called Parallel Two-Phase K-means (Par2PK-means), parallelizes IKM by employing the model of Two-Phase K-means. Testing with large data sets, this algorithm attains a good speedup ratio, closing to the linearly speed-up ratio.

Neural networks have seen limited use in prediction for high-dimensional data with small sample sizes, because they tend to overfit and require tuning many more hyperparameters than existing off-the-shelf machine learning methods. With small modifications to the network architecture and training procedure, we show that dense neural networks can be a practical data analysis tool in these settings. The proposed method, Ensemble by Averaging Sparse-Input Hierarchical networks (EASIER-net), appropriately prunes the network structure by tuning only two L1-penalty parameters, one that controls the input sparsity and another that controls the number of hidden layers and nodes. The method selects variables from the true support if the irrelevant covariates are only weakly correlated with the response; otherwise, it exhibits a grouping effect, where strongly correlated covariates are selected at similar rates. On a collection of real-world datasets with different sizes, EASIER-net selected network architectures in a data-adaptive manner and achieved higher prediction accuracy than off-the-shelf methods on average.

This paper focuses on Crime zone Identification. Then, it clarifies how we conducted the Belief Rule Base algorithm to produce interesting frequent patterns for crime hotspots. The paper also shows how we used an expert system to forecast potential types of crime. In order to further analyze the crime datasets, the paper introduces an analysis study by combining our findings of the Chittagong crime dataset with demographic information to capture factors that could affect neighborhood safety. The results of this solution could be used to raise awareness of the dangerous locations and to help agencies predict future crimes at a specific location in a given time.

Text generation often requires high-precision output that obeys task-specific rules. This fine-grained control is difficult to enforce with off-the-shelf deep learning models. In this work, we consider augmenting neural generation models with discrete control states learned through a structured latent-variable approach. Under this formulation, task-specific knowledge can be encoded through a range of rich, posterior constraints that are effectively trained into the model. This approach allows users to ground internal model decisions based on prior knowledge, without sacrificing the representational power of neural generative models. Experiments consider applications of this approach for text generation. We find that this method improves over standard benchmarks, while also providing fine-grained control.

The last decade saw the emergence of systematic large-scale replication projects in the social and behavioral sciences, (Camerer et al., 2016, 2018; Ebersole et al., 2016; Klein et al., 2014, 2018; Collaboration, 2015). These projects were driven by theoretical and conceptual concerns about a high fraction of "false positives" in the scientific publications (Ioannidis, 2005) (and a high prevalence of "questionable research practices" (Simmons, Nelson, and Simonsohn, 2011). Concerns about the credibility of research findings are not unique to the behavioral and social sciences; within Computer Science, Artificial Intelligence (AI) and Machine Learning (ML) are areas of particular concern (Lucic et al., 2018; Freire, Bonnet, and Shasha, 2012; Gundersen and Kjensmo, 2018; Henderson et al., 2018). Given the pioneering role of the behavioral and social sciences in the promotion of novel methodologies to improve the credibility of research, it is a promising approach to analyze the lessons learned from this field and adjust strategies for Computer Science, AI and ML In this paper, we review approaches used in the behavioral and social sciences and in the DARPA SCORE project. We particularly focus on the role of human forecasting of replication outcomes, and how forecasting can leverage the information gained from relatively labor and resource-intensive replications. We will discuss opportunities and challenges of using these approaches to monitor and improve the credibility of research areas in Computer Science, AI, and ML.

Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.

Google today launched Read Along, an Android app that taps AI and machine learning to help children learn to read by providing verbal and visual feedback. Preliminary research suggests that apps like Read Along could significantly improve children's reading skills. A three-month pilot of Read Along's predecessor -- Bolo -- in the Unnao district of India involving 1,500 children across 200 villages found that, compared with a control group, 39% of the app's users reached the highest level of the Annual Status of Education Report (ASER) reading assessment test and 64% saw an increase in scores. Moreover, 92% of parents said they noticed some improvement in their child's skills. Read Along comes preloaded with around 500 stories and interactive games within those stories, for which kids earn stars and badges as they learn, practice, and progress.

It's also the first aircraft "to be designed, engineered and manufactured in Australia in more than 50 years," Boeing said in a statement. Australian Prime Minister Scott Morrison said the drones will protect the country's pricier combat aircraft like F-35 stealth fighters and their pilots in the future, and drone production will help with a current crisis, fighting the effects of the coronavirus. "The Loyal Wingman program has helped support around 100 high-tech jobs in Australia. Such projects will be critical to bolster growth and support jobs as the economy recovers from the Covid-19 pandemic," Morrison said in a statement. The Australian government says it has invested about $40 million into the project.

Smartphone giant LG has announced its upcoming mid-range flagship, the Velvet, as it launches in South Korea. The phone will feature a "raindrop" triple-camera array, where the lenses are ordered in descending size on the back of the phone, and has a 6.8-inch OLED display with a notch in the centre. As documented in older posts from the technology giant, that main camera will be comprised of 48MP, 8MP, and 5MP cameras and the front-camera is rumoured to have a 16MP resolution. Powering the phone will be the 5G-compatible Qualcomm's 765 processor, found in competing devices from Oppo and Motorola, and Google's Pixel 5. Only one variety of the phone has been announced, with 8GB of processing power and 128GB of storage.

Post-human design runs the risk of obscuring the fact that AI technology actually imports a Cartesian humanist logic, which subsequently influences how we design and conceive of so-called smart or intelligent objects. This leads to unwanted metaphorical attributions of human qualities to smart objects. Instead, starting from an embodied sensemaking perspective, designers should demand of engineers to radically transform the very structure of AI technology, in order to truly support critical posthuman values of collectivity, relationality and community building.