Reviewerly/exHarmony huggingface dataset:
Managing Artificial Intelligence on Public Transportation (Case Study Jakarta City, Indonesia)

This research aims to study AI management and its implementation in the public transportation sector to accommodate the needs of Jakarta's citizens. The basic framework used in this research is on analysis of case study transportation in Jakarta. Using a qualitative method with data collection from literature review and observation. Data analysis focuses on challenges and requirements needed to implement AI on public transportation to enhance urban mobility. Along with key AI management aspects discussed on 1) Source funding for AI in Mass Public Transport Project; 2) Reform Traditional Procurement Process; 3) Develop Unbiased AI; 4) Avoid the trap of over-relying on AI; 5) Develop Legal and Policy Framework. The purpose of this research is that it can contribute as a reference and recommendation for local government in making policy on AI and transportation in the future. Based on the research analysis, it is concluded that AI can address transportation problems in traffic management, traffic safety, public transportation, and urban mobility. However, AI implementation in Jakarta needs more improvement to ensure the system is integrated among many types of public transports, and improve its user-friendliness. It is recommended to develop a one-gate integrated application system to access all information on public transportation and to develop a legal and policy framework related to the applications.
Unsupervised Lifelong Learning with Curricula

Lifelong machine learning (LML) has driven the development of extensive web applications, enabling the learning systems deployed on web servers to deal with a sequence of tasks in an incremental fashion. Such systems can retain knowledge from learned tasks in a knowledge base and seamlessly apply it to improve the future learning. Unfortunately, most existing LML methods require labels in every task, whereas providing persistent human labeling for all future tasks is costly, onerous, error-prone, and hence impractical. Motivated by this situation, we propose a new paradigm named unsupervised lifelong learning with curricula (ULLC), where only one task needs to be labeled for initialization and the system then performs lifelong learning for subsequent tasks in an unsupervised fashion. A main challenge of realizing this paradigm lies in the occurrence of negative knowledge transfer, where partial old knowledge becomes detrimental for learning a given task yet cannot be filtered out by the learner without the help of labels. To overcome this challenge, we draw insights from the learning behaviors of humans. Specifically, when faced with a difficult task that cannot be well tackled by our current knowledge, we usually postpone it and work on some easier tasks first, which allows us to grow our knowledge. Thereafter, once we go back to the postponed task, we are more likely to tackle it well as we are more knowledgeable now. The key idea of ULLC is similar – at any time, a pool of candidate tasks are organized in a curriculum by their distances to the knowledge base. The learner then starts from the closer tasks, accumulates knowledge from learning them, and moves to learn the faraway tasks with a gradually augmented knowledge base. The viability and effectiveness of our proposal are substantiated through extensive empirical studies on both synthetic and real datasets.
An ASR System for Malayalam Short Stories using Deep Neural Network in KALDI

Speech being a natural mode of communication for humans is still a challenging problem for Speech Recognition (SR) using a computer. Recently the advancement of technology and deep learning models made a tremendous advancement in Automatic Speech Recognition (ASR) and applied in different regional languages also. This study explored Automatic Speech Recognition using KALDI for continuous Malayalam short stories with different algorithms. Malayalam is the native language of Kerala state in Indian sub-continent. Very less amount of transcribed data is available for Malayalam, so it is recognized as a low resource language. The database consists of twenty Malayalam short stories with transcription. For building such an ASR system the Mel Frequency Cepstral Coefficient (MFCC) features are excerpted with its transformation like Linear Discriminant Analysis (LDA), Maximum Likelihood Linear Transform (MLLT) are extracted from the Malayalam speech database. SGMM + DNN system is modeled and trained for speech utterances.The model's output is measured and compared to the Word Error Rate (WER). A substantial decrement in WER is reported for the DNN model.
ComQA: Compositional Question Answering via Hierarchical Graph Neural Networks

With the development of deep learning techniques and large scale datasets, the question answering (QA) systems have been quickly improved, providing more accurate and satisfying answers. However, current QA systems either focus on the sentence-level answer, i.e., answer selection, or phrase-level answer, i.e., machine reading comprehension. How to produce compositional answers has not been throughout investigated. In compositional question answering, the systems should assemble several supporting evidence from the document to generate the final answer, which is more difficult than sentence-level or phrase-level QA. In this paper, we present a large-scale compositional question answering dataset containing more than 120k human-labeled questions. The answer in this dataset is composed of discontiguous sentences in the corresponding document. To tackle the ComQA problem, we proposed a hierarchical graph neural networks, which represent the document from the low-level word to the high-level sentence. We also devise a question selection and node selection task for pre-training. Our proposed model achieves a significant improvement over previous machine reading comprehension methods and pre-training methods. Codes, dataset can be found at https://github.com/benywon/ComQA.
Generalizing Discriminative Retrieval Models using Generative Tasks

Information Retrieval has a long history of applying either discriminative or generative modeling to retrieval and ranking tasks. Recent developments in transformer architectures and multi-task learning techniques have dramatically improved our ability to train effective neural models capable of resolving a wide variety of tasks using either of these paradigms. In this paper, we propose a novel multi-task learning approach which can be used to produce more effective neural ranking models. The key idea is to improve the quality of the underlying transformer model by cross-training a retrieval task and one or more complementary language generation tasks. By targeting the training on the encoding layer in the transformer architecture, our experimental results show that the proposed multi-task learning approach consistently improves retrieval effectiveness on the targeted collection and can easily be re-targeted to new ranking tasks. We provide an in-depth analysis showing how multi-task learning modifies model behaviors, resulting in more general models.
A Finnish turn: Digital and synthesiser musical instruments

This paper will follow a musically experimental trajectory from non-mediated musical sound through many centuries of musical innovation from the simplest forms of resonation to today’s synthesised musics in electronic – digital and synthesiser musics – with side looks at how changes in musical technologies play roles in the player-instrument and listener-music relations. I shall then look briefly at the modern, electric amplification of ‘electric’ instruments and much ‘louder’ musics with their equally radical changes in audience-performance situations. Finally, then, I will turn to electronic variants, which yet again drastically change the musical gestalt of player-instrument and listener-music relations.
IoT Product Pleasurability - Investigating the Pleasurable User Experiences Between Conventional Products and IoT Products Through Watches

The arrival of Internet of Things (IoT) overcomes limitations of time and space by providing ubiquitous accessibility of its products. Design and HCI research are challenged by an increasingly complex network of diverse types of interaction. To design pleasurable user experiences (UXs), new models need to be developed for emerging IoT products as previous models for conventional products might not be applicable anymore. From a human-centred perspective, this project investigates how the pleasurable UXs will change after a product develops into an IoT product. The project aims at understanding the attributes of IoT products that might contribute to understand the future relationship between users and IoT objects. The project applies UX theories by Jordan (a hierarchy of consumer needs, 2003) and Hassenzahl (top-ten psychological needs, 2010) as theoretical guidelines. These theories classified the contribution of human factors to design pleasurable products and agreed that the enjoyments from the psychological level are at the top of UX. The project uses two online questionnaires to collect data on 1) the UX of Smartwatches and 2) conventional Wristwatches (digital and analogue), in order to reflect on the influence of IoT products on the pleasurable UXs. The results show that the UXs of IoT Watches and conventional watches were not significantly different in terms of the four kinds of pleasure as proposed by Jordan; however, IoT products and conventional products did appear to influence some items in top-ten psychological needs differently.
Blockchain-based Proof of Existence (PoE) Framework using Ethereum Smart Contracts

In recent years, Blockchain, underpinned by distributed ledger technology (DLT) has been touted as the next disruptive technology with the potential to revolutionise various industry verticals and horizontals. Plagiarism and Intellectual Property Infringements of copyrights of artifacts, trade secrets, etc., are often fought in courts of law. There is an inherent need to adduce reliable evidence to establish a prima facie tort case or even beyond. In this paper we aim to leverage on the Blockchain technology to provide a digital transformation in the post-Covid world by offering a new platform to aid in the protection of one's intellectual property rights through a Proof of Existence (PoE) framework using Ethereum smart contracts. We have developed a seamless web platform to allow users experience a simple yet secure Proof of Existence (PoE) service by allowing the users to (i) certify, (ii) manage and (iii) view their documents securely through a digital portfolio. This PoE service leverages on the Blockchain characteristics to provide a reliable and transparent means to record a tamper-proof evidence of copyright information with timestamp as proof of existence for all its transactions through smart contracts.
Intrusion Detection of Industrial Control System Based on Double-layer One-class Support Vector Machine

In this paper, the stealthy attack detection in industrial control system (ICS) is studied, and a new detection method is proposed from the perspective of signal analysis. The method consists of a double-layer one-class support vector machine model (DL-OCSVM), where the first-layer model is the standard OCSVM, and the second-layer model is obtained by incremental learning based on the former. The wavelet decomposition is used to extract the physical characteristics of the ICS. The KKT condition and the adjacent classification interval are adopted to reduce the training samples, improving the learning rate and system scalability. In addition, the designed boundary samples are employed for incremental learning, avoiding overfitting and reducing false positives rate (FPR). The experimental results show that the proposed method has high detection rate and low FPR for stealthy attacks, and is more suitable for precision machining process.
Lexicon Based Document Level Sentiment Analysis on the Multilingual Dataset

Sentiment Analysis is a potential and high-speed emergent research topic as it deals with the mining of social users’ opinions which generates incredibly in fraction of seconds. The hasty growth of online medium such as blogs and various social networking sites are nowadays used by many users to express their perspectives on various topics. Most of us are more comfortable in expressing our views and opinions in native language. Commonly using positive and negative English words might also be used along with the native language. Here the authors propose a method to perform polarity calculation on the multilingual dataset considered for sentiment. The multilingual dataset under consideration consists of a mix of reviews in English and Malayalam. Various language detectors are considered for experimentation and the langid algorithm gives more accurate results. The English and Malayalam lexicons need to be treated differently. Since English is a Universal language, the positive and negative lexicons and the list of stopwords are readily available. But the positive and negative Malayalam lexicons need to be created in house. The proposed methodology treats both types of lexicons differently for sentiment analysis.
A Steganography Embedding Method Based on P<sub>single</sub> / P<sub>double</sub> and Huffman Coding

The highest priority in the era of information and communication technology is to effectively and accurately hide information when considering steganography. The development of such a system for hiding sensitive messages should not leave any evidence of liability concerning the hidden message. The most critical challenges are the security and capacity issues in the steganography system. Hence, it is necessary to maintain the system's peak-to-noise ratio (PSNR) to make it tangible, imperceptible, and robust. This paper proposed an enhanced steganography system via P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</sub> /P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">double</sub> method. The enhanced system aims to increase the capacity and security of the existing problems. Three main stages are used to achieve the system's objectives, namely: 1) data preparation stage passing by embedding stage, 2) evaluation stage, and 3) the extracting stage. The enhanced method is evaluated using the standard datasets, which include Lena, Peppers, Baboon, and Cameraman. The obtained results of PSNR (78.159) verified the worthiness of the proposed method.
From What Is Promised to What Is Experienced with Intelligent Bots

Based on survey research, this study, aims to identify the gap between what is promised (usefulness) and what consumers experience (usability and enjoyment) from virtual assistants such as voice- or chat-based bots. Our empirical results compare these three factors applied to AI bots and their adoption for customer service. They were collected with a sample of 148 participants in the financial sector (utilitarian experience), and with an exploratory sample of 112 exposed to a tourism voice bot offering (hedonic experience). Interesting comparisons (similarities and differences) are highlighted to provide managerial user experience (UX) guidance for the design and evaluation of virtual assistants from a user/customer-centric perspective.
Using Ansible as Part of TOSCA Orchestrator

Cloud computing is used in many areas and by many specialists to ease application distribution and deployment. Cloud providers basically aim at providing resources varying from others. So it becomes very difficult to deploy multicloud applications or move to the cloud from own hardware clusters. TOSCA standard offers cloud resources model that unifies many cloud providers. In this paper we propose TOSCA orchestrator Clouni. Clouni takes TOSCA templates from the user and generates Ansible playbooks. Ansible is then used as a configuration tool for deployment automation. Clouni makes it easy for cloud users to deploy complex topologies.
Self-segmentation of pass-phrase utterances for deep feature learning in text-dependent speaker verification

In this paper, we propose a novel method to segment and label pass-phrase utterances for training deep neural network (DNN) bottleneck (BN) features for text-dependent speaker verification (TD-SV). Specifically, gender-dependent hidden Markov models (HMMs) for monophones are first trained using the pass-phrase utterances that are disjoint from evaluation. Next, the trained HMMs are speaker-adapted and then used for segmenting and labeling these training utterances at the phone level. The resulted labeled data is subsequently used for training DNN models to discriminate gender-dependent phones for the purpose of extracting phone-discriminant BN features. This is in contrast to conventional approaches that apply a general-purpose, speaker-independent automatic speech recognition (ASR) system for generating segmentation and labels. The proposed method eliminates the need for a separate ASR system, which can additionally have the disadvantage of mismatch with the pass-phrase utterances in terms languages, dialects, domains, acoustic conditions and so on. Experiments are conducted on the RedDots challenge 2016 database of TD-SV using short utterances with Gaussian mixture model-universal background model and i-vector techniques. Experimental results demonstrate that the proposed method yields lower error rates in TD-SV when compared to a set of existing methods. A thorough ablation study further confirms the effectiveness of the method. Fusion in both score and feature levels also shows the complementary nature of the proposed features.
Swift shadow paging (SSP): no write-protection but following TLB flushing

Virtualization is a key technique for supporting cloud services and memory virtualization is a major component of virtualization technology. Common memory virtualization mechanisms include shadow paging and hardware-assisted paging. The shadow paging model needs to synchronize shadow/guest page tables whenever there is a guest page table update. In the design of traditional shadow paging (TSP), the guest page table pages are write-protected so the updates can be intercepted by the hypervisor to ensure synchronization. Frequent page table updates cause lots of VM_Exits. Researchers have developed hardware-assisted paging to eliminate this overhead. However, address translation needs to walk a two-dimensional page table. This design significantly increases the overhead of page walk.
DCT-net: A deep co-interactive transformer network for video temporal grounding

Language-guided video temporal grounding is to temporally localize the best matched video segment in an untrimmed long video according to a given natural language query (sentence). The main challenge in this task lies in how to fuse visual and linguistic information effectively. Recent works have shown that the attention mechanism is beneficial to the multi-modal feature fusion process. In this paper, we present a concise yet valid Deep Co-Interactive Transformer Network (DCT-Net) which repurposes a Transformer-style architecture to sufficiently model cross modality interactions. It consists of Co-Interactive Transformer (CIT) layers cascaded in depth for multi-step interactions between a video-sentence pair. With the help of the proposed CIT layer, both visual and language features can share the mutually improved benefits from each other. Extensive experiments on two public datasets, i.e. ActivityNet-Caption and TACOS, demonstrate the effectiveness of our proposed model compared to state-of-the-art methods.
Defining spatial agents

By studying current research into agents the people have selected the following types of agent as the basis for spatial agent development: mobile agents, interface agents, deliberative (cognitive) agents and reactive agents. This chapter is concerned with the application of agents to spatial issues, the people will only discuss the types of agent that will be useful in this area of expertise: mobile agents, reactive agents, cognitive agents and interface agents. Agents performing spatial data mining, after receiving the specification of their search, should travel through the network looking for the data that the user needs. The aim is to identify what makes a tool adequate for (spatial) agent development and not to provide a complete list of tools and languages. It is a very simple prototype, but its implementation was very useful for the authors because it raised some additional questions about the development of spatial agents.
Design and Analysis of a Secure Coded Communication System Using Chaotic Encryption and Turbo Product Code Decoder

Errors in a transmitted message is unavoidable since noise is inevitable in any communication channel. For reliable transmission of messages, the bit error rate has to be kept at an acceptable rate by the use of proper error control coding schemes. To ensure that the transmission is also secure, data encryption is used as an integral part of the system. This paper deals with the design and analysis of a secure and reliable communication system accomplished using logistic map-based chaotic encryption and turbo product codes. The system is simulated using MATLAB and it is shown that the use of encryption for secure communication does not degrade the system performance. The hardware design of the decoder is also done and verified in Verilog using the same set of vectors as obtained from the system simulation. BER performance was analyzed in all the different scenarios and the correctness of the design was established.
A Temperature Data Denoising Method Using Laplacian Matrix and Neumann Series

In this paper, a temperature data denoising method using Laplacian matrix and Neumann series is presented. First, the denoising problem is formulated as an optimization problem whose solution needs to solve the matrix inversion. Then, to avoid solving matrix inversion, the Neumann series is used to approximate the matrix inversion by a truncated series expansion. Next, two efficient distributed implementation methods are presented to realize the proposed denoising algorithm. Finally, the real temperature data in Taiwan is used to demonstrate the effectiveness of the proposed method.
Media Attention and Bitcoin Prices

We present a dual process on a diffusion model to examine whether Bitcoin prices behave with jumps attributed to informative signals derived from Twitter and Google Trends. The empirical results indicate that Bitcoin prices are partially driven by a momentum on media attention in social networks, justifying a sentimental appetite for information demand.
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles

With recent advances in autonomous driving, voice control systems have become increasingly adopted as human-vehicle interaction methods. This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS). Prior work has shown that Siri, Alexa and Cortana, are highly vulnerable to inaudible command attacks. This could be extended to ADAS in real-world applications and such an inaudible command threat is difficult to detect due to microphone nonlinearities. In this paper, we aim to develop a more practical solution by using camera views to defend against inaudible command attacks where ADAS are capable of detecting their environment via multi-sensors. To this end, we propose a novel multimodal deep learning classification system to defend against inaudible command attacks. Our experimental results confirm the feasibility of the proposed defense methods and the best classification accuracy reaches 89.2%. Code is available at https://github.com/ITSEG-MQ/Sensor-Fusion-Against-VoiceCommand-Attacks.