Reviewerly/exHarmony huggingface dataset:
State-Time-Alignment Phone Clustering Based Language-independent Phone Recognizer Front-end for Phonotactic Language Recognition

The now-acknowledged sensitive of Phonotactic Language Recognition (PLR) technology to the performance of the phone recognizer front-end have spawned interests to develop many methods to improve it. In this paper a state-of-art State-Time-Alignment (STA) phone clustering approach to build language-independent phone recognizer is proposed in phonotactic language recognition system to balance the performance and the complexity of the speech tokenizing processing in PLR.Experiments are carried out on the database of National Institute of Standards and Technology language recognition evaluation 2009 (NIST LRE 2009) and the experimental results have confirmed that phonotactic language recognition system using the collaborated language model yields 1.84%, 5.55% and 16.82% in equal error rate (EER), which show that the STA phone clustering based phone recognizer front-end outperforms the original English and Mandaren phone recognizers and other phone clustering methods based phone recognizer.
All or In-cloud: How the Identification of Six Types of Anomalies Is Affected by the Discretization Method

Anomaly detection is the process of identifying cases, or groups of cases, that are in some way unusual and do not fit the general patterns present in the dataset. Numerous algorithms use discretization of numerical data in their detection processes. This study investigates the effect of the employed discretization method on the unsupervised detection of each of the six anomaly types acknowledged in a recent typology of data anomalies. To this end, experiments are conducted with various datasets and SECODA, a general-purpose algorithm for unsupervised non-parametric anomaly detection in datasets with numerical and categorical attributes. This algorithm employs discretization of continuous attributes, exponentially increasing weights and discretization cut points, and a pruning heuristic to detect anomalies with an optimal number of iterations. The empirical results of experiments with synthetic and real-world data demonstrate that standard SECODA can detect all six types, but that different discretization methods favor the discovery of certain anomaly types. These main findings also hold for other detection techniques using discretization.
Hierarchical Functional Signcryption: Notion and Construction

With the purpose of achieving fine-grained access control over the signing and decryption capabilities in the context of a traditional digital signcryption scheme, the concept of functional signcryption (FSC) is introduced by Datta et al. (ProvSec 2015) to provide the functionalities of both functional encryption (FE) and functional signature (FS) in an integrated paradigm. In this paper, we introduce the notion of hierarchical functional signcryption (HFSC), which augments the standard functional signcryption with hierarchical delegation capabilities on both signcrypting and unsigncrypting, thereby significantly expanding the scope of functional signcryption in hierarchical access-control application. More precisely, our contributions are two-fold: (i) we formalize the syntax of HFSC and its security notion, (ii) we provide a generic construction of HFSC based on cryptographic building blocks including indistinguishability obfuscation (iO) and statistically simulation-sound non-interactive zero-knowledge proof of knowledge (SSS-NIZKPoK) for NP relations, and we formally shows that it satisfies selective message confidentiality and selective ciphertext unforgeability.
Crypto-Currencies: From the Fetishism of Gold to Hayek Gold

Based on the Marxist perspective of money and particularly Marx’s analysis of the fetishism of money and capital, Nakatani and Mello grapple with the nature of crypto-currencies, especially Bitcoin, highlighting their explosive growth in 2017 as a part of the dynamics of the overaccumulation of capital, which has been circling the world for decades, particularly in financial markets, in search of new and old assets that might serve as the means for the appropriation of fictitious income and which have taken on the most foolish and fetishized forms.
Teaching reform on compiling principal course

The compiling principal course is a key class in universities. To deepen knowledge in limited time, and develop the aggregation in relative course, we propose a new method for compiler theory course. Students can learn the basic knowledge from textbook in different prospects. The teacher will use cases in relative courses to explain knowledge points. Furthermore, they can refer to the videos on course website and MOOC websites. The students will obtain comprehensive theory knowledge and practice ability.
Feasibility of Algae Photobioreactor as Façade in the Office Building in Indonesia

Abstract Algae has a high potential as a renewable energy resource. Recent studies about algae photobioreactor application in the building façade seem promising and the first algae building has been constructed in Hamburg, Germany. However, the application of algae photobioreactor in the hot and humid climate is still limited. This paper will discuss the feasibility of algae photobioreactor as façade in the office building by evaluating the model from Sketchup software. This is a real project of a new office building, named ITB Innovation Park at Institut Teknologi Bandung campus, Jalan Ganesha no. 15. The algae photobioreactor is planned to be installed in the west façade as shading devices and renewable energy resources. It is assumed that the acrylic moulding would work better to reduce leakage issues and the possibility of killing the algae.
Sequential Change Detection Based on Universal Compression for Markov Sources

A universal compression code can act as an estimator for the distribution of a finite alphabet finite-order Markov source. This property of universal codes was exploited by Jacob and Bansal in [5] to propose a modification of the CUSUM test in order to solve the change detection problem when the post-change distribution is not known. The performance of this test was proven to be asymptotically optimal for a memoryless sources and class of sources with memory under Lorden's minimax formulation. This study was further extended in [9] where performance of the modified CUSUM for an i.i.d. setting under Lai's criterion involving a constraint on the probability of false alarm within a window (PFAW) was analyzed. In this paper, we introduce a modified version of the window limited CUSUM (WL-CUSUM) test by incorporating strongly universal code. We closely follow the work of Lai [8] in order to prove the asymptotic optimality for the test under the PFAW criterion. We further prove the asymptotic optimality of the modified WL-CUSUM test in the Bayesian setting.
A use case of humanware and cloud-based cyberinfrastructure

Humanware, the human component of cyberinfrastructure, is focused on understanding and developing the human expertise needed to support computationally-based research with the goal of maximizing efficiency, productivity, and return on investment associated with cyberinfrastructure. In this paper, we present an example use case describing the humanware challenges associated with leveraging cloud-based cyberinfrastructure to implement a machine learning software framework that classifies ambiguous time-series data sets. Our project demonstrates that collaboration between researchers and cyberinfrastructure experts significantly advanced our empirical research efforts and maximized the return on investment by utilizing a cost-efficient cloud-based cyberinfrastructure.
A Survey on Predicting Resident Intentions Using Contextual Modalities in Smart Home

The Smart Home is an environment that enables the resident to interact with home appliances which provide resident intended services. In recent years, predicting resident intention based on the contextual modalities like activity, speech, emotion, object affordances, and physiological parameters have increased importance in the field of pervasive computing. Contextual modality is the feature through which resident interacts with the home appliances like TVs, lights, doors, fans, etc. These modalities assist the appliances in predicting the resident intentions making them recommend resident intended services like opening and closing doors, turning on and off televisions, lights, and fans. Resident-appliance interaction can be achieved by embedding artificial intelligence-based machine learning algorithms into the appliances. Recent research works on the contextual modalities and associated machine learning algorithms which are required to build resident intention prediction system have been surveyed in this article. A classification taxonomy of contextual modalities is also discussed.
Attention Based Joint Model with Negative Sampling for New Slot Values Recognition

Natural Language Understanding (NLU) is an important component of a task oriented dialogue system, which obtains slot values in user utterances. Hou, Mulan NLU module is often required to return standard slot values and recognize new slot values at the same time in many real world dialogue such as restaurant booking. Wang, Xiaojie Neither previous sequence labeling models nor classifiers can satisfy both requirements by themselves. Yuan, Caixia To address the problem, the paper proposes an attention based joint model with negative sampling. Yang, Guohua It combines a sequence tagger with a classifier by an attention mechanism. Hu, Shuo The tagger helps in identifying slot values in raw texts and the classifier simultaneously maps them into standard slot values or the symbol of new values. Negative sampling is used for constructing negative samples of existing values to train the model. Shi, Yuanyuan Experimental results on two datasets show that our model outperforms the previous methods. The negative samples contribute to new slot values identification, and the attention mechanism discovers important information and boosts the performance.
Macro and Micro Reinforcement Learning for Playing Nine-ball Pool

We present a method of training a reinforcement learning agent to play nine-ball pool. The training process uses a combination of reinforcement learning, deep neural networks and search trees. These technologies have achieved tremendous results in discrete strategy board games, and we extend their applications to pool games, which is a complicated continuous case. Pool types of games have a huge action space, to improve the efficiency of exploration, we use a macro and micro action framework to combine reinforcement learning and the search tree. The agent learns skills such as choosing pockets and control the post-collision position. Our method shows the potential to solve billiards planning problems through AI.
A Course on Applied AI and Data Science

Artificial intelligence (AI) and data science have become one of the most popular curricula in the computing educations. Plenty of theories, optimizations and math are involved in these courses, which results in a higher degree of difficulty for students to learn, not to mention the students without specializations in computer science or information technology. Beyond the complicated knowledge and theories, students may prefer to learn and focus on applied AI or data science which refer to the knowledge or skills for practical problem-solving and real-world applications. For example, information retrieval has been listed as one of the curricula in several undergraduate and/or graduate programs in the AI or data science programs. This paper describes a course that focuses on the topic of recommender systems which is in high demand in both academia and industries. This course has been extremely successful at the authors' institutions. In this paper, we introduce the course's objectives, structure and methodologies, discuss possible ways to deliver hands-on practice, summarize the outcomes, and finally present the lessons learned, as well as the feedbacks from the students. These experience could be useful and may give advice to other educators looking to create a similar course in their program.
There Is Nothing to See. Or Is There?

Augmented Reality has the potential of transforming teaching. By applying this technology themselves, students progress from consumers of technology to producers of their own digital content, and they can make it available to a large audience and create an immersive learning experience. After a language trip to Scotland, teachers and two classes of 17-year-old students from a school in Salzburg, Austria, decided to present the outcome of their project work from their stay abroad in a more interactive and innovative way. Instead of inviting parents and friends to an evening of PowerPoint, the students designed AR posters and visitors were invited to an interactive gallery walk during which they had the chance to explore thematic posters related to Scotland. While at first glance the posters seem rather bland, digital content, which the students produced, opens up by scanning the pictures on the poster with a special AR app. This project successfully combines language and digital skills and has shown to motivate students to engage even deeper with their topic.
Conditional usability testing for UX optimization

This paper describes a novel conditional testing approach to optimize usability tests. To use this method, we need to revisit conventional usability testing approaches to redefine task based classification methods, conduct tests using efficient time tracking systems, and define time based logical algorithms to gauge user expertise. This approach is designed to increase the number of tasks that can be tested in a given time which reduces the number of iterations, thus optimizing the usability testing process.
Wireless IoT-Based Intrusion Detection Using LIDAR in the Context of Intelligent Border Surveillance System

Patrolling borders of India has become a tedious and risky task for military and para-military personnel in order to control Intrusions. With the advancements in wireless IoT (Internet of Things) technology, it has become much easier to design, develop and deploy a cost-effective, automatic and efficient system for intrusion detection in the context of surveillance. Making the system suitable for all types of environments, the light detection and ranging (LIDAR) sensor is developed for the acquisition of distance with a range of 40 m from the position where an object resides. Data collected by the sensor is monitored and administered in a server. Software required for the analysis of data and generation of alert notifications is deployed in the server which is an added feature to the system and assists the concerned security personnel to respond quickly and engage the safety.
Distorting Political Communication: The Effect Of Hyperactive Users In Online Social Networks

Online Social Networks (OSNs) are used increasingly for political purposes. Among others, politicians externalize their views on issues, and users respond to them, initiating political discussions. Part of the discussions are shaped by hyperactive users. These are users that are over-proportionally active in relation to the mean. In this paper, we define the hyperactive user on the social media platform Facebook, both theoretically and mathematically. We apply a geometric topic modelling algorithm (GTM) on German political parties' posts and user comments to identify the topics discussed. We prove that hyperactive users have a significant role in the political discourse: They become opinion leaders, as well as set the content of discussions, thus creating an alternate picture of the public opinion. Given that, we discuss the dangers of replicating the specific bias by statistical and deep learning algorithms, which are used widely for recommendation systems and the profiling of OSN users.
Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting

Diabetes is one of the most serious diseases which is becoming increasingly common in recent years. Diabetes can be treated and its consequences are prevented or delayed if predicted timely. This paper investigates an evolutionary computation approach for diabetes prediction. By utilizing the multi-objective Genetic Programming Symbolic Regression, the prediction accuracy level of 79.17% is achieved. Two utilized objectives are namely prediction accuracy and complexity level of the created model (i.e., formula). Moreover, a majority-voting scheme is proposed and compared with other conventional classification algorithms. A widely studied dataset for diabetes prediction, the Pima Indian Diabetes dataset shared in University of California Irvine dataset repository, has been selected for conducting our experimental studies. The work presented here has profound implications for future applications of diabetes prediction and may one help to solve the problem of diabetes by their timely prediction.
Analyzing Textual Information at Scale

We overview recent advances in textual analysis for social sciences. Count-based economic model, structured statistical tool, and plain-vanilla machine learning apparatus each has merits and limitations. To take a data-driven approach to capture complex linguistic structures while ensuring computational scalability and economic interpretability, a general framework for analyzing large-scale text-based data is needed. We discuss recent attempts combining the strengths of neural network language models such as word embedding and generative statistical modeling such as topic modeling. We also describe typical sources of texts, the applications of these methodologies to issues in finance and economics, and promising future directions.
Chinese Event Factuality Detection

There are a large number of expression forms and semantic information in natural language, which contain fake, speculative, and fuzzy statements. Identifying event factuality is vital to various natural language applications, such as information extraction and knowledge base population. Most of existing methods for Chinese event factuality detection adopt shallow lexical and syntactic features to determine the factuality of target event via end-to-end classification models. Although such methods are easy to implement, they ignore the linguistic features related to event factuality, which limits the performances on this task. On this basis, we introduce three kinds of linguistic features to represent event factuality, including factuality cue, event polarity, and tense. Then, we employ a CNN-based feature encoder to capture their latent feature representations automatically. Finally, we integrate three kinds of features with word embeddings to identify the factuality label of target event. The experimental results show that our method achieves 94.15% of accuracy, with 12.34% of improvement on the state-of-the-art. In addition, we also demonstrate and analyze the effectiveness of three linguistic features for Chinese event factuality detection.
Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution

Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow sigmoid-type neural networks that achieve 100% accuracy in classification for datasets following a linear separability condition. The separability condition in this work is more relaxed than the widely used linear separability. Moreover, the constructed neural network guarantees perfect classification for any datasets sampled from a separable probability distribution. This generalization capability comes from the saturation of sigmoid function that exploits small margins near the boundaries of intervals formed by the separable probability distribution <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Multi-view reconstructive preserving embedding for dimension reduction

With the development of feature extraction technique, one sample always can be represented by multiple features which are located in different high-dimensional spaces. Because multiple features can reflect one same sample from various perspectives, there must be compatible and complementary information among the multiple views. Therefore, it’s natural to learn information from multiple views to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reduction method named multi-view reconstructive preserving embedding (MRPE) in this paper. MRPE reconstructs each sample by utilizing its k nearest neighbors. The similarities between each sample and its neighbors are mapped into lower-dimensional space in order to preserve the underlying neighborhood structure of the original manifold. MRPE fully exploits correlations between each sample and its neighbors from multiple views by linear reconstruction. Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding. Various evaluations based on the applications of document classification, face recognition and image retrieval demonstrate the effectiveness of our proposed approach on multi-view dimension reduction.
Detection of human-interaction network using Markov random field

Discovering network structures among social actors is one of the most fundamental issues related to social networks. In this paper, we propose a novel and effective algorithm for building a human-interaction network from the location data of individuals gathered by sensors such as the GPS system. We model the problem using Markov random field. The proposed approach combines statistical machine learning with sparse modeling, i.e., the L1 regularized maximum likelihood approach. We demonstrate the validity of our method through numerical experiments using artificial location data generated from a simulator of quasi-human-transfer.
Deep Learning Algorithms for Accurate Prediction of Image Description for E-commerce Industry

Currently, the demand for automation in information technology industry is increasing at rapid rate. Leading companies in artificial intelligence and computer vision have started investing in the research and making new products that can easily do the redundant work done by humans, ultimately increasing the productivity and reducing the cost of doing work. Industrial artificial intelligence can create new business models, can automate the industrial tasks which are redundant and can be easily done by specifically trained machines. The work is experimented with various feature extraction methods, like Visual Geometry Group (VGG-16) and VGG-19, and a smaller proposed five-layer convolution neural network (CNN-5) to generate captions for an image. The performances of these methods are compared using BLEU-1, BLEU-2, BLEU-3 and BLEU-4 scores. Various experiments have been conducted to see the effect of different hyper-parameters, like number of layers, learning rate, dropout, on the model accuracy measured by BLEU score. The effect of the input length (15, 34 and 40) of the sequence of text data on the LSTM network is also being analyzed and reported in the results section. The experiments are conducted on benchmark Flickr-8 K dataset which contains 8000 images and their respective descriptions in a text file. Each image was described by five different people and the text file contains image id and their respective five descriptions for each of the 8000 images.