Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation

In its daily use, the Indonesian language is riddled with informality, that is, deviations from the standard in terms of vocabulary, spelling, and word order. On the other hand, current available Indonesian NLP models are typically developed with the standard Indonesian in mind. In this work, we address a style-transfer from informal to formal Indonesian as a low resource machine translation problem. We build a new dataset of parallel sentences of informal Indonesian and its formal counterpart. We benchmark several strategies to perform style transfer from informal to formal Indonesian. We also explore augmenting the training set with artificial forward-translated data. Since we are dealing with an extremely low-resource setting, we find that a phrase-based machine translation approach outperforms the Transformer-based approach. Alternatively, a pre-trained GPT-2 fined-tuned to this task performed equally well but costs more computational resource. Our findings show a promising step towards leveraging machine translation models for style transfer. Our code and data are available in https://github.com/haryoa/stif-indonesia.
Compressing Convolutional Neural Networks by Pruning Density Peak Filters

With the recent development of GPUs, the depth of convolutional neural networks (CNNs) has increased, and its structure has become complex. Hence, it is challenging to deploy them into a hardware device owing to its immense computational cost and memory for storage parameters. We propose a method of pruning a filter located near the density peak, which grasps the density of the filter space for each layer to overcome this problem. The density is calculated in the filter space based on the number of neighboring filters within a certain distance around the filter and the distance to a denser space. Moreover, we do not remove all filters at once, but use a method of pruning a certain number iteratively, so that filters can be evenly pruned in multiple locations with high density inside the filter space. After that, we fine-tune the pruned network to restore their performance. The experimental results show the effectiveness of the proposed method with respect to the other methods using CIFAR-10, and ImageNet dataset on VGGNet and ResNet architecture. Notably, on CIFAR-10, our method reduces 60.8% of FLOPs on ResNet56 with 0.31% validation accuracy improvement. Moreover, we achieve up to 51.9% FLOPs reduction with a little accuracy drop on ImageNet for ResNet34.
Quantum Computing based Implementation of Full Adder

Quantum computing is the most advanced form of computing that is evolving today. Classical computers work on binary bits - 0 or 1, but quantum computers work on quantum bits or qubits which can be in the state 0 and 1 simultaneously and exhibit properties like superposition and entanglement. This paper proposes the implementation of a full adder with the concepts of quantum computing using quantum gates on IBM composer. IBM composer is the choice of tool for this paper, and the resultant speed of operation is calculated to be 6.064ms for 1024 shots (1 shot is 100 repetitions). By designing a full adder in this paper, it is contemplated that more complicated digital circuitry will be implemented on quantum computers in near future, thus paving the way for bringing high speed quantum computing in our daily lives to solve critical problems that are unresolvable by conventional computing.
Development and Evaluation of a Blockchain Concept for Production Planning and Control in the Semiconductor Industry

Blockchain technology promises several benefits for operations management, but reported use cases, concepts and applications are rare. This contribution indicates the findings from a case study of analyzing the suitability of blockchain technology for semiconductor manufacturing at the 300 mm wafer frontend facility of Infineon Technologies in Dresden, Germany. This plant contains one of the most highly automated fabrication lines in the industry. Here, several different and isolated digital software tools are used to control manufacturing, all accessing centralized databases with different views and restrictions. We assess if and how a blockchain solution could and should be designed to enhance the analytic capabilities for this facility using the design science approach and evaluate the economic value of the designed application using the analytical hierarchy process.
Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multi-modal, multi-sensor target tracking.In this Multiple Target Tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely.This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately.The proposed method is based around particle Belief Propagation (BP), a recent but highly efficient framework for tracking multiple targets.The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another.We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a Random Finite Set (RFS) approach, namely the Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter.The results show the proposed method is 17% more accurate than the RFS approach on average.
Subspace learning for facial expression recognition: an overview and a new perspective

For image recognition, an extensive number of subspace-learning methods have been proposed to overcome the high-dimensionality problem of the features being used. In this paper, we first give an overview of the most popular and state-of-the-art subspace-learning methods, and then, a novel manifold-learning method, named soft locality preserving map (SLPM), is presented. SLPM aims to control the level of spread of the different classes, which is closely connected to the generalizability of the learned subspace. We also do an overview of the extension of manifold learning methods to deep learning by formulating the loss functions for training, and further reformulate SLPM into a soft locality preserving (SLP) loss. These loss functions are applied as an additional regularization to the learning of deep neural networks. We evaluate these subspace-learning methods, as well as their deep-learning extensions, on facial expression recognition. Experiments on four commonly used databases show that SLPM effectively reduces the dimensionality of the feature vectors and enhances the discriminative power of the extracted features. Moreover, experimental results also demonstrate that the learned deep features regularized by SLP acquire a better discriminability and generalizability for facial expression recognition.
Brief Paper: Design and Implementation of a Smart Glass Application for XR Assisted Training of Core Nursing Skills

Extended reality-assisted training offers repeatable learning opportunities at a low cost.This paper proposes a smart glass application for training core nursing skills to nursing students, who often need to memorize and practice training sequences.The proposed smart glass application interactively presents a series of instructions to help students remember and perform two core nursing skills in the correct order.We conducted a usability test on 30 undergraduate nursing students in their third year using the smart glass application.Our initial findings show that many students have positively evaluated the possibility of using smart glasses for training, but have also encountered several challenges with the smart glass application's user interface, which takes time to adapt.
A Semantic-Enabled Smart Home for AAL and Continuity of Care

The rise of Internet of Things (IoT) technologies provided the means to enable the smart home (SH), a residence aimed at anticipating and responding to its dwellers’ needs and one of the most promising Ambient Assisted Living (AAL) solutions. However, the massive adoption of monitoring techniques and some shortcomings in the field of security can hinder the adoption of IoT-based SH solutions. In this work, we describe how we have addressed the challenges arising from the fields of AAL and continuity of care, by creating the Smart Human-Centred Living Environment Lab, an innovative prototype of a SH that can address the specificities of each inhabitant. The system on which the SH relies is a knowledge-based system that leverages the semantic representations of relevant concepts and data, thus allowing for the customization of services according to each inhabitant’s needs and preferences, without the need of continuous monitoring. A semantic middleware ensures the semantic interoperability of the information and thus the realization of an IoT-based architecture optimizing the provision of each service. Finally, digital applications and virtual reality-based systems are integrated in the SH to provide support to the activities of daily living and the execution of rehabilitative exercises, in the perspective of continuity of care. The system tries to address the issues of other SHs, in which privacy concerns, stigma, and ageism may hinder the use of innovative technologies in daily life. However, a few issues still remain: among these, the validation of the whole system in terms of users’ acceptance and the possibility of providing such a service on a large scale.
A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment

We propose a new approach to Human Activity Evaluation (HAE) in long videos using graph-based multi-task modeling. Previous works in activity evaluation either directly compute a metric using a detected skeleton or use the scene information to regress the activity score. These approaches are insufficient for accurate activity assessment since they only compute an average score over a clip, and do not consider the correlation between the joints and body dynamics. Moreover, they are highly scene-dependent which makes the generalizability of these methods questionable. We propose a novel multi-task framework for HAE that utilizes a Graph Convolutional Network backbone to embed the interconnections between human joints in the features. In this framework, we solve the Human Activity Segmentation (HAS) problem as an auxiliary task to improve activity assessment. The HAS head is powered by an Encoder-Decoder Temporal Convolutional Network to semantically segment long videos into distinct activity classes, whereas, HAE uses a Long-Short-Term-Memory-based architecture. We evaluate our method on the UW-IOM and TUM Kitchen datasets and discuss the success and failure cases in these two datasets.
RobotVQA — A Scene-Graph- and Deep-Learning-based Visual Question Answering System for Robot Manipulation

Visual robot perception has been challenging to successful robot manipulation in noisy, cluttered and dynamic environments. While some perception systems fail to provide an adequate semantics of the scene, others fail to present appropriate learning models and training data. Another major issue encountered in some robot perception systems is their inability to promptly respond to robot control programs whose realtimeness is crucial.This paper proposes an architecture to robot vision for manipulation tasks that addresses the three issues mentioned above. The architecture encompasses a generator of training datasets and a learnable scene describer, coined as RobotVQA for Robot Visual Question Answering. The architecture leverages the power of deep learning to predict and photo-realistic virtual worlds to train. RobotVQA takes as input a robot scene's RGB or RGBD image, detects all relevant objects in it, then describes in realtime each object in terms of category, color, material, shape, openability, 6D-pose and segmentation mask. Moreover, RobotVQA computes the qualitative spatial relations among those objects. We refer to such a scene description in this paper as scene graph or semantic graph of the scene. In RobotVQA, prediction and training take place in a unified manner. Finally, we demonstrate how RobotVQA is suitable for robot control systems that interpret perception as a question answering process.
Robust motion blur kernel parameter estimation for star image deblurring

Under dynamic conditions, the star images may be blurred and result in the decrease of attitude measurement accuracy of the star sensor. To estimate blur kernel parameters needed for star image deblurring, including blur angle and blur length, a method based on sparse representation, hyper-Laplacian priors, and ensemble neural network is proposed. First, under the constraint of the hyper-Laplacian image prior, the blur angle is estimated by using the quasi-convex characteristics between the sparse representation coefficients and the blur angle. Then, the ensemble back-propagation neural network trained by the bagging method is exploited to estimate the blur length. Finally, we recover the star image by a non-blind deblurring algorithm. To validate the proposed algorithm, we test the algorithm on star images and compare the algorithm with several existing deconvolution algorithms. The results reveal that our approach outperforms other algorithms in terms of effectiveness and robustness.
IoT Based Sign Language Recognition System

Sign language is the key communication medium, which deaf and mute people use in their day - to-day life. Talking to disabled people will cause a difficult situation since a non-mute person cannot understand their hand gestures and in many instances, mute people are hearing impaired. Same as Sinhala, Tamil, English, or any other language, sign language also tend to have differences according to the region. This paper is an attempt to assist deaf and mute people to develop an effective communication mechanism with non-mute people. The end product of this project is a combination of a mobile application that can translate the sign language into digital voice and loT-enabled, light-weighted wearable glove, which capable of recognizing twenty-six English alphabet, digits, and words. Better user experience provides with voice-to-text feature in mobile application to reduce the communication gap within mute and non-mute communities. Research findings and results from the current system visualize the output of the product can be optimized up to 25 % -35 % with an enhanced pattern recognition mechanism.
Classification of rice leaf diseases using artificial neural network

Abstract Rice is one of the important food crops for humans. The yield of this crop is affected by several factors, one of them is climate change. The irregularities of climate can cause the crops were infected by various diseases. By knowing the diseases through its unusual characteristics, fast remedial actions could be expected. The purpose of this essay is to find the best artificial neural network (ANN) architecture in classifying brown spot and leaf smut diseases on rice crops, two common types of diseases attack rice plant, based on the leaf images. Two features of rice leaf images i.e. shape and color were extracted to classify both diseases. The result of this research demonstrated the best ANN architecture to classify the diseases is the architecture with 4 neurons in the input layer, 5 neurons in one single hidden layer, and one neuron in the output layer. This architecture has accuracy as much as 66.3 percent.
Blockchain Consistency Check Protocol for Improved Reliability

Blockchain is a technology that provides security features that can be used for more than just cryptocurrencies.Blockchain achieves security by saving the information of one block in the next block.Changing the information of one block will require changes to all the next block in order for that change to take effect.Which makes it unfeasible for such an attack to happen.However, the structure of how blockchain works makes the last block always vulnerable for attacks, given that its information is not saved yet in any block.This allows malicious node to change the information of the last block and generate a new block and broadcast it to the network.Given that the nodes always follow the longer chain wins rule, the malicious node will win given that it has the longest chain in the network.This paper suggests a solution to this issue by making the nodes send consistency check messages before broadcasting a block.If the nodes manage to successfully verify that the node that generated a new block hasn't tampered with the blockchain than that block will be broadcasted.The results of the simulation show suggested protocol provided better security compared to the regular blockchain.
Conversion to Remote Proctoring of the Community English Language Program Online Placement Exam at Teachers College, Columbia University

The online placement test for the Community English Language Program (CLP) at Teachers College, Columbia University has been administered in a computer lab since 2015. While most of the components of this placement exam system are online, including registration, exam delivery, scoring, and score reporting, the COVID-19 pandemic elevated the need to find a solution to the final component, a secure test administration in a location other than our computer lab. Our solution was remote proctoring by means of a third-party software, Honorlock. This software provided us with the ability to review issues flagged by the system during administration. Despite the reliance on technology to deliver an exam to test takers around the world, we encountered very few technological issues, which was due, in part, to the technical support team at Honorlock. The high-tech security software was instrumental in administering our low stakes placement exam during the pandemic.
The burdens and the benefits: Socio-economic impacts of mobile phone ownership in Tanzania

Mobile phones figure prominently in discussions about international development and poverty alleviation, but existing research is ambiguous about its ultimate effects. We report on a qualitative investigation of mobile phone impact based on 133 semi-structured interviews with low- to middle-income individuals in Dar es Salaam and Arusha Region, Tanzania. We find that impacts on mobile users are indeed mixed, but that they can be categorized by their phone use habits: Simple Users use phones for socializing and entertainment; Deliberate Users additionally use their phones for work and business purposes; Advanced Users make striking technical use of their phones. Only the latter two – comprising a minority of our participants – appear to be benefiting financially from mobile phone use. We also find evidence that for some, mobile phone use competes with basic needs such as food. Our findings suggest one way to reconcile the divergent claims in that technology has disparate impact on different individuals. We discuss implications for policy and development interventions.
The PANDA Framework for Hierarchical Planning

Abstract During the last years, much progress has been made in hierarchical planning towards domain-independent systems that come with sophisticated techniques to solve planning problems instead of relying on advice in the input model. Several of these novel methods have been integrated into the PANDA framework , which is a software system to reason about hierarchical planning tasks. Besides solvers for planning problems based on plan space search, progression search, and translation to propositional logic, it also includes techniques for related problems like plan repair, plan and goal recognition, or plan verification. These various techniques share a common infrastructure, like e.g. a standard input language or components for grounding and reachability analysis. This article gives an overview over the PANDA framework, introduces the basic techniques from a high level perspective, and surveys the literature describing the diverse components in detail.
Learning a Weighted Classifier for Conditional Domain Adaptation

Domain adaptation is critical for solving the problem in new, unseen environments. Recent works have shown that adversarial domain adaptation achieves state-of-the-art performance. However, existing adversarial domain adaptation networks usually pursue extracting the domain-invariant features to achieve adaptation, which breaks the discriminative structures of the model and leads to negative transfer. To this end, we propose a novel approach named Learning a Weighted Classifier for Conditional Domain Adaptation (LWC). Specifically, we propose a novel mechanism to quantify the transferability of each sample. We observe that it is hard for domain discriminator to distinguish similar samples, indicating a higher entropy of its output. And for dissimilar samples, the entropy of domain discriminator’s output is lower. By leveraging domain entropy of conditioned domain discriminator, we adopt matched strategies for both similar samples and dissimilar samples, which learns a more distinct boundary on similar samples and alleviates the effect of dissimilar samples. Extensive experiments on open benchmarks verify that our model is able to outperform previous methods.
Constructions of quantum MDS codes

Let $${\mathbb {F}}_q$$ be a finite field with $$q=p^{e}$$ elements, where p is a prime number and $$e \ge 1$$ is an integer. In this paper, by means of generalized Reed–Solomon codes, we construct two new classes of quantum maximum-distance-separable ( quantum MDS) codes with parameters $$\begin{aligned}{}[[q + 1, 2k-q-1, q-k+2]]_q \end{aligned}$$ for $$\lceil \frac{q+2}{2}\rceil \le k\le q+1$$ , and $$\begin{aligned}{}[[n,2k-n,n-k+1]]_q \end{aligned}$$ for $$n\le q $$ and $$ \lceil \frac{n}{2}\rceil \le k\le n$$ . Our constructions improve and generalize some results available in the literature. Moreover, we give an affirmative answer to the open problem proposed by Fang et al. (Finite Fields Appl 53: 85–98, 2018).
EVET: Enhancing Visual Explanations of Deep Neural Networks Using Image Transformations

Numerous interpretability methods have been developed to visually explain the behavior of complex machine learning models by estimating parts of the input image that are critical for the model's prediction. We propose a general pipeline of enhancing visual explanations using image transformations (EVET). EVET considers transformations of the original input image to refine the critical input region based on an intuitive rationale that the region estimated to be important in variously transformed inputs is more important. Our proposed EVET is applicable to existing visual explanation methods without modification. We validate the effectiveness of the proposed method qualitatively and quantitatively to show that the resulting explanation method outperforms the original in terms of faithfulness, localization, and stability. We also demonstrate that EVET can be used to achieve desirable performance with a low computational cost. For example, EVET-applied Grad-CAM achieves performance comparable to Score-CAM, which is the state-of-the-art activation-based explanation method, while reducing execution time by more than 90% on VOC, COCO, and ImageNet.