Reviewerly/exHarmony huggingface dataset:
Towards Multi-resident Activity Monitoring with Smarter Safer Home Platform

This chapter demonstrates a system that can turn a normal house to a smart house for daily activity monitoring with the use of ambient sensors. We first introduce our smarter safer home platform and its applications in supporting independent livings of seniors in their own home. Then we show a proof of concept which includes a novel activity annotation method through voice recording and deep learning techniques for automatic activity recognition. Our multi-resident activity recognition system (MRAR) is designed to support multiple occupants in a house with minimum impact on their living styles. We evaluate the system in a real house lived by a family of three. The experimental results show that it is promising to develop a smart home system for multiple residents which is portable and easy to deploy.
MDD Approach for Mobile Applications Based On DSL

Mobile application development is a fastest-growing domain in the software industry in recent years. The rapid development of hardware and software platforms for smartphones has resulted in a large number of mobile platforms (e.g. Apple iOS, Android OS, Windows phone, and BlackBerry OS). This diversity of platforms constitutes a challenge for application developers, who must write the same application separately for each operating system. MDD (Model Driven Development) approach based on DSL (Domain Specific Language), could provide a possible solution to solve this problem. In this proposal, we present a model-driven development method to develop native mobile applications. This approach proposes a new DSL to define GUI (Graphical User Interface) independently of the target mobile platforms and allows developers to automatically generate native code to these several platforms. Currently, our method has been implemented to support Android OS. The usefulness of the proposed approach is demonstrated through a concrete case study.
Effective Representation for Easy-First Dependency Parsing

Easy-first parsing relies on subtree re-ranking to build the complete parse tree. Whereas the intermediate state of parsing processing is represented by various subtrees, whose internal structural information is the key lead for later parsing action decisions, we explore a better representation for such subtrees. In detail, this work introduces a bottom-up subtree encoding method based on the child-sum tree-LSTM. Starting from an easy-first dependency parser without other handcraft features, we show that the effective subtree encoder does promote the parsing process, and can make a greedy search easy-first parser achieve promising results on benchmark treebanks compared to state-of-the-art baselines. Furthermore, with the help of the current pre-training language model, we further improve the state-of-the-art results of the easy-first approach.
Known-plaintext attack on JTC-based linear cryptosystem

The original image in JTC-based cryptosystem can be recovered without any degradation after the application of some denoising operations. However, after these operations, the ciphertext becomes linear with respect to the input image. A known-plaintext attack is performed on the linear cryptosystem. The encryption key is accurately calculated from two non-specially selected known plaintext-ciphertext pairs. Computation cost of the attacking procedure is reduced by sampling the ciphertext according to the sampling theorem. Simulation results show the validity and feasibility of the known-plaintext attack.
A Deep Reinforcement Learning Based Human Behavior Prediction Approach in Smart Home Environments

Human behaviors(activities) recognition is a hot research topic for many researchers in recent years. In this paper, we propose an approach to recognize human activities by deep reinforcement learning (DL) algorithm. We use a collection of some publicly available real-life datasets from the smart home environment domain. Based on selected predictive model architecture and Deep Q-network (DQN), the human behaviors recognition results are envaluated, the experiment result shows that the proposed deep learning algorithm is an effective way for recognizing human activities in smart home.
Pairwise Teacher-Student Network for Semi-Supervised Hashing

Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed. Pairwise similarity is easily obtained and widely used for retrieval, and most supervised hashing algorithms are carefully designed for the pairwise supervisions. As labeling all data pairs is difficult, semi-supervised hashing is proposed which aims at learning efficient codes with limited labeled pairs and abundant unlabeled ones. Existing methods build graphs to capture the structure of dataset, but they are not working well for complex data as the graph is built based on the data representations and determining the representations of complex data is difficult. In this paper, we propose a novel teacher-student semi-supervised hashing framework in which the student is trained with the pairwise information produced by the teacher network. The network follows the smoothness assumption, which achieves consistent distances for similar data pairs so that the retrieval results are similar for neighborhood queries. Experiments on large-scale datasets show that the proposed method reaches impressive gain over the supervised baselines and is superior to state-of-the-art semi-supervised hashing methods.
Fast QR code detection based on BING and AdaBoost-SVM

In industry 4.0, the most popular way to identify and track objects is to add tags. Because the cost of smart tag is still high, most companies still use cheap barcodes or QR codes. In order to solve the real-time positioning problem of upgrading traditional tags into smart tags, this paper proposes a QR tag location method based on Binarized Normed Gradients (BING) and AdaBoost-SVM. BING algorithm is the fastest general object detection algorithm at present, but its disadvantage is that the recall rate decreases sharply with the increase of Intersection-over-Union (IoU) threshold. In the proposed algorithm, Adaboost-SVM method is introduced to make up for the shortcomings of BING algorithm. More specifically, before training and prediction process of Adaboost-SVM, Contrast Limited Adaptive Histogram Equalization (CLAHE) mechanism is used for image enhancement, and thus can greatly shorten the training time and improve the precision of prediction. As a result, the precision of low-quality image prediction is significantly improved. Compared with the existing methods based on Neural Network (NN), the proposed algorithm does not depend on the parallel acceleration hardware such as GPU, hence, it can significantly reduce the hardware cost, and expand QR code automatic positioning algorithm utilization in the field of lower hardware requirements.
A Task-oriented Chatbot Based on LSTM and Reinforcement Learning

Traditional conversational chatbots usually adopt a retrieved-based model. Developers have to provide a large amount of conversational data and classify those data to different intents. To avoid cumbersome development processes, we propose a method to build a chatbot by a sentence generation model which generates sequence sentences based on the generative adversarial network. The architecture of our model contains a generator that generates a diverse sentence, and a discriminator that judges the sentences between the generated and the raw data. In the generator, we combine the attention model that responses for tracking conversational states with the sequence-to-sequence model using hierarchical long-short term memory to extract sentence information. For the discriminator, we calculate twotypes of rewards to assign low rewards for repeated sentences and high rewards for diverse sentences. Extensive experiments are presented to demonstrate the utility of our model which generates more diverse and information-rich sentences than those of the existing approaches.
Catapa Resume Parser

This paper proposes a method to solve the problem of extracting contents from a resume, especially for Indonesian resumes using segmentation method by header followed by models for each corresponding headers. An end to end resume extraction system is created using some heuristic rules and machine learning algorithms to solve the problem. On average, an accuracy of ~91.41% is achieved for personal information entities (name, email, phone, gender, date of birth, and religion), ~68.47% accuracy for job experiences entities (company, job title, start date, and end date), and ~80.85% accuracy for educations entities (institution, major, level, start date, end date, and GPA) out of 221 random resumes using the aforementioned method.
Neural text normalization with adapted decoding and POS features

Abstract Text normalization is the task of mapping noncanonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. This task is especially important for languages such as Swiss German, with strong regional variation and no written standard. In this paper, we propose a novel solution for normalizing Swiss German WhatsApp messages using the encoder–decoder neural machine translation (NMT) framework. We enhance the performance of a plain character-level NMT model with the integration of a word-level language model and linguistic features in the form of part-of-speech (POS) tags. The two components are intended to improve the performance by addressing two specific issues: the former is intended to improve the fluency of the predicted sequences, whereas the latter aims at resolving cases of word-level ambiguity. Our systematic comparison shows that our proposed solution results in an improvement over a plain NMT system and also over a comparable character-level statistical machine translation system, considered the state of the art in this task till recently. We perform a thorough analysis of the compared systems’ output, showing that our two components produce indeed the intended, complementary improvements.
Topic Modeling on Indonesian Online Shop Chat

This paper aims to discover topics from an Indonesian online shop chat. Moreover, we employed Latent Dirichlet Allocation to find out what kind of topics that are often discussed and conversation trends between buyers and customer service. Several tasks were performed, such as, collecting data, preprocessing, phrase aggregation, topic modeling, and topic analysis. We found several attracting findings during our experiments. In preprocessing task, product name extraction from URLs assisted to discover the intended product from the customer's conversation. On the other hand, the phrase aggregation task helped us to merge various terms which have same intended meaning, so that, we could obtain better topical model result and easier to determine the topic label.
Exploring the Uncanny Valley Theory in the Constructs of a Virtual Assistant Personality

Dr. Masahiro Mori’s theory of the Uncanny Valley has been used since the 1970’s to portray humans’ desire of the amount of humanness vs. machineness that artificial intelligence (AI) should have. However, it has usually referenced AIs such as robots and avatars, but what happens when it comes to current Virtual Assistants (VAs)? And how about when it is regarding a very human characteristic such as VA personality? This study presents an exploration of the Virtual Assistants personality, specially of two of its constructs, words content and voice, in order to identify if the Uncanny Valley theory applies to Virtual Assistants and explicitly to their personality. This research study analyses what users expect of a VA personality in terms of how much it should resemble to a human one. This study concludes by establishing the results of the research activities undertaken which show that users expect that the Virtual Assistant personality should have a balance between human and machine, not leaning towards either extreme, exemplifying the relevance and timeliness of the Uncanny Valley theory.
Image enlargement method based on cubic surfaces with local features as constraints

Abstract The quality of image edges and textures plays a vital role in the visual effect of images. For keeping the features of image edges and textures better, we propose an image enlargement method based on cubic fitting surfaces with local features as constraints. The cubic fitting surface constructed with the edge and the distance as constraints effectively improves the approximation accuracy and shape retention, resulting in sharper edges in the initially enlarged image. The error between the cubic fitting surface and the input image surface leads to the error between the initially enlarged image and the input image. Using image self-similarity to filter the initially enlarged image can further improve its quality. In the iterative optimization process, the cubic surface with higher fitting accuracy is constructed on smaller regions of low resolution error images, which effectively improves the quality of the enlarged image and accelerates the convergence speed. Because the algorithm effectively utilizes the local feature information, compared with other state-of-the-art methods, the new method not only has higher PSNR/SSIM in numerical accuracy, but also has clear edges and textures visually.
The Statistical Minkowski Distances: Closed-Form Formula for Gaussian Mixture Models

The traditional Minkowski distances are induced by the corresponding Minkowski norms in real-valued vector spaces. In this work, we propose statistical symmetric distances based on the Minkowski's inequality for probability densities belonging to Lebesgue spaces. These statistical Minkowski distances admit closed-form formula for Gaussian mixture models when parameterized by integer exponents.
Reducing the Memory Cost of Training Convolutional Neural Networks by CPU Offloading

In recent years, Convolutional Neural Networks (CNNs) have enabled unprecedented progress on a wide range of computer vision tasks.However, training large CNNs is a resource-intensive task that requires specialized Graphical Processing Units (GPU) and highly optimized implementations to get optimal performance from the hardware.GPU memory is a major bottleneck of the CNN training procedure, limiting the size of both inputs and model architectures.In this paper, we propose to alleviate this memory bottleneck by leveraging an under-utilized resource of modern systems: the device to host bandwidth.Our method, termed CPU offloading, works by transferring hidden activations to the CPU upon computation, in order to free GPU memory for upstream layer computations during the forward pass.These activations are then transferred back to the GPU as needed by the gradient computations of the backward pass.The key challenge to our method is to efficiently overlap data transfers and computations in order to minimize wall time overheads induced by the additional data transfers.On a typical work station with a Nvidia Titan X GPU, we show that our method compares favorably to gradient checkpointing as we are able to reduce the memory consumption of training a VGG19 model by 35% with a minimal additional wall time overhead of 21%.Further experiments detail the impact of the different optimization tricks we propose.Our method is orthogonal to other techniques for memory reduction such as quantization and sparsification so that they can easily be combined for further optimizations.
A Smart Book Management System Based on Blockchain Platform

Based on the application of blockchain technology, this paper proposes a smart library management system based on blockchain technology. The system eliminates the possibility of tampering with data by the unique workload proof mechanism and consensus mechanism in blockchain technology, and solves the problem of low efficiency and poor security of the central system by distributed accounting. At the same time, the danger of damage to the central database is dispersed, and data security is guaranteed.
On Building Design Guidelines for An Interactive Machine Learning Sandbox Application

There are several machine learning suites that are readily-available. However, these applications require a basic foundation in machine learning making them appear difficult to configure. We introduce a sandbox approach with the goal of designing alternative programming interactions for machine learning tasks. A set of guidelines have been drafted and supported with user interviews to validate the proposed design framework. Ten students with novice machine learning experience participated in the study to formulate a programming pipeline that was used to draft the guidelines based on the literary review. A problem statement was formed from the analysis of interview insights using UX Research techniques. The insights suggest that a visual sandbox approach helps reduce the learning curve of programming machine learning tasks. The design guidelines we drafted focused on the three design factors namely system intent, interaction, and algorithm visualization. Considering these guidelines, a prototype was produced that will undergo future testing and validation.
Sequentialising Nested Systems

In this work, we investigate the proof theoretic connections between sequent and nested proof calculi. Specifically, we identify general conditions under which a nested calculus can be transformed into a sequent calculus by restructuring the nested sequent derivation (proof) and shedding extraneous information to obtain a derivation of the same formula in the sequent calculus. These results are formulated generally so that they apply to calculi for intuitionistic, normal modal logics and negative modalities.
Information Security Risks, Benefits, and Mitigation Measures in Cloud Sourcing

This paper develops a categorization of cloud security risks, elaborates how they impact information security, and discusses potential security benefits from cloud sourcing. This review integrates the literature on information systems and computer science to summarize managerial and technological mitigation measures that enhance cloud security. The analysis uncovers gaps regarding the empirical investigation of security considerations in the corporate decision-making process. Specifically, the micro level of how security comes into play in the decision-making process and whether mitigation measures proposed by scholars are practically feasible requires further investigation. Furthermore, the macro level, how stakeholders perceive cloud sourcing, is not yet well understood.
Network Alignment by Representation Learning on Structure and Attribute

Network alignment is the task of recognizing similar network nodes across different networks, which has many applications in various domains. As traditional network alignment methods based on matrix factorization do not scale to large graphs, a variety of representation learning based approaches has been proposed recently. However, these techniques tend to focus on topology consistency between two networks while ignoring other valuable information (e.g. network nodes attribute), which makes them susceptible to structural changes. To alleviate this problem, we propose RAN, a representation-based network alignment model that couples both structure and node attribute information. Our framework first constructs multi-layer networks to represent topology and node attribute information, then computes the alignment result by learning the node embeddings for source and target network. The experimental results show that our method is able to outperform other techniques significantly even on large datasets.
Efficient Near-Optimal Variable-Size Microaggregation

Microaggregation is a well-known family of statistical disclosure control methods, that can also be used to achieve the k-anonymity privacy model and some of its extensions. Microaggregation can be viewed as a clustering problem where clusters must include at least k elements. In this paper, we present a new microaggregation heuristic based on Lloyd's clustering algorithm that causes much less information loss than the other microaggregation heuristics in the literature. Our empirical work consistently observes this superior performance for all minimum cluster sizes k and data sets tried.
A Deep Learning Approach for Robust Corridor Following from an Arbitrary Pose

Classical visual servoing involves two stages. Selected features are first extracted from a captured image, and are then passed through a control law that computes a control signal for servoing. To perform autonomous corridor following via visual servoing, a robust feature extraction output is necessary, which is often difficult to obtain reliably in dynamically changing environments. Moreover, in cases where the selected features are not present in the image frame, the outcome of the control law is unclear. In order to address this drawback of the classical approach, we introduce a novel end-to-end CNN based approach for autonomous corridor following. We carry out this task on wheelchair platform developed at our institute, and perform a rigorous statistical and practical evaluation of our method. Additionally, we also study a particularly interesting case of initiating the corridor following task using our CNN based approach and perform a comparative analysis against the fail case of the classical approach.
A Quantitative Methodology for Business Process-Based Data Privacy Risk Computation

The imminent introduction of the Data Protection Act in India would make it necessary for almost all enterprises, dealing with personal data, to implement privacy-specific controls. These controls would serve to mitigate the risks that breach the privacy properties of user data. Hence, the first step toward implementing such controls is the execution of privacy risk assessment procedures that would help elicit the privacy risks to user data. All user data are processed/managed by one or more business processes. Hence, assessment of privacy risks to user data should consider the vulnerabilities within, and threats to, corresponding business process. It should also consider different perspectives, namely business, legal and contractual needs, and users’ expectations, during the computation of data privacy values. This paper proposes such a comprehensive methodology for identifying data privacy risks and quantifying the same. The risk values are computed at different levels (privacy property level, business process level, etc.) to help both senior management and operational personnel, in assessing and mitigating privacy risks.
Vector Addressing for Non-Sequential Sampling in Fir Image Filtering

Image filtering is fundamental in image processing. The acceleration is essential since image resolution has highly increased. For the acceleration, image subsampling is a general approach for any filtering. However, this approach has a drawback in accuracy due to image aliasing. Several papers moderate this problem by sub-sampling image or filtering kernel non-sequential sampling. In this paper, we improve the sampling combined with image and kernel subsampling. We also accelerate the work of non-sequential sampling by vector addressing of hardware acceleration. Experimental results show that the proposed method accelerate bilateral filtering and adaptive Gaussian filtering. Also, the proposed vector addressing accelerate both filters.