Reviewerly/exHarmony huggingface dataset:
FANG: Fast and Efficient Successor-State Generation for Heuristic Optimization on GPUs

Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. The implementation of such a function can become arbitrarily complex in the field of combinatorial optimization. Many N(x) functions for a huge variety of different domain-specific problems have been developed in the past to solve this general problem. However, it has always been a great challenge to port or realize these functions on a massively-parallel architecture like a Graphics Processing Unit (GPU). We present a GPU-based method called FANG that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization. It can be customized to satisfy domain-specific requirements and leverages the underlying hardware in a fast and efficient way by construction. Moreover, our method has a high scalability with respect to the number of input states and the complexity of a single state. Measurements show significant performance improvements compared to traditional exploration approaches leveraging the CPU on our evaluation scenarios.
GA for QoS Satisfaction Degree Optimal Web Service Composition Selection Model

As the Web service technology is using more widely in cloud computing, the Web service composition selection has also become one of the most popular research fields. The service composition selection problem is described as an multi-objective optimization problem considering multi QoS parameters and solved by an intelligent optimization algorithm in most of the existing literatures. Feelings of the users are usually ignored. In this paper, the concept of QoS satisfaction degree is introduced and adopted as the optimal objective of the Web service composition selection model. Genetic algorithm (GA) is applied to solve the problem and test result show that the solution prompted in this paper is feasible and effective.
An Immersive Service Guide for Home Appliances

Virtual reality is increasingly used in a wide range of applications due to the capabilities of immersive presentation and interaction with realistic 3D content. A broad and potentially prominent area of application of VR are multimedia service and operation guides for home appliances, addressed to both technicians and end-users. In comparison to traditional textual, audio and video guides, VR-based interactive guides can be more adaptable, precise, user-friendly and attractive, which are advantages to both producers and consumers. However, due to the difficulty associated with the creation of such guides, they still gain little attention of manufacturers and distributors of products. In this paper, we present an immersive service guide that illustrates scenarios of repairing and testing induction hobs. The service guide has been developed in the Unity game engine, and runs on smartphones with inexpensive headsets and gesture tracking devices. The guide has been elaborated in cooperation with Amica S.A., which is a major household appliance producer in Poland. The guide can be an important innovation in training of technical staff as well as end users, at the same time serving as a marketing tool, which can attract customers as well as technicians.
Part-guided Network for Pedestrian Attribute Recognition

Pedestrian attribute recognition, which can benefit other tasks such as person re-identification and pedestrian retrieval, is very important in video surveillance related tasks. In this paper, we observe that the existing methods tackle this problem from the perspective of multi-label classification without considering the spatial location constraints, which means that the attributes tend to be recognized at certain body parts. Based on that, we propose a novel Part-guided Network (P-Net), which guides the refined convolutional feature maps to capture different location information for the attributes related to different body parts. The part-guided attention module employs the pix-level classification to produce attention maps which can be interpreted as the probability of each pixel belonging to the 6 pre-defined body parts. Experimental results demonstrate that the proposed network gives superior performances compared to the state-of-the-art techniques.
An Exceedingly Fast Model for Low Resolution Handwritten Digit String Recognition

In this paper, we present a simplified target detector in character level for low resolution handwritten digit string recognition (HDSR). By utilizing the bounding boxes, some difficulties such as connecting, overlapping and arbitrary length can be well solved. The designed model takes advantage of residual network and anchor boxes of digital shape to recognize the images effectively and accurately. Experiment results show that the accuracy on unknown length strings can be up to 95.6%, meanwhile it has an extremely fast speed, which is up to 257 FPS on 32*160 px resolution images when using a Tesla P100 GPU.
Learning Calculus with Augmented Reality and Virtual Environments

In this paper, we present how spatial visualization skills can be developed in engineering students, using augmented reality and remote virtual environments in calculus courses. Two tools have been specifically developed for this purpose: AVRAM (Remote Virtual Environments for the Learning of Mathematics), which allows the visualization and manipulation of surfaces in a virtual three-dimensional space and ARC (Augmented Reality in Calculus), which uses activity cards for each multivariable calculus topic. This paper describes the type of skills that can be developed with the use of these two apps in the dynamic visualization framework and presents some activities that have the purpose of developing logical thinking and problem solving and spatial abstraction skills. More than 1200 students have used these apps. The results of their use in calculus classes indicate an increase in students' engagement, visualization skills and a significant improvement in final grades.
A DenseNet feature-based loop closure method for visual SLAM system

Loop closure is a crucial part in SLAM, especially for large and long-term scenes. Utilizing off-the-shelf networks’ features in loop closure becomes a hot spot. However, what kind of network is more suitable in loop closure and how to use their features have not been well-studied. In this paper, DenseNet is introduced in this field according to its own characters. The features of DenseNet preserve both semantic information and structure details and outweigh other popular networks’ features significantly. Based on this, a DenseNet feature-based framework, named Dense-Loop, is proposed to address the loop closure problem. Weighted Vector of Locally Aggregated Descriptor (WVLAD) method is used to encode the local descriptors as the final global descriptor, which could resist geometry structure and viewpoint changes. Furthermore, 4 max-pooling by channel and locality-sensitive hashing (LSH) are adopted to accelerate the search process. Extensive experiments are conducted on public datasets and the results demonstrate Dense-Loop could achieve state-of-the-art performance.
Statistical and Linguistic Decision-Making Techniques Based on Fuzzy Set Theory

The article describes the production of sentences with the help of several grammars interesting for stenographs processing. Grammars and types of rules are determined by a theory with a high degree of formalization. A statistical approach to the distribution of stenographs is proposed. It can be used in cases when the available information is insufficient to describe the symbols or classes that may be included in the data set being under consideration. It is shown that decision-making is similar to hierarchical division, but in the latter case the decision in each vertex is made on the basis of statistical properties of the corresponding feature. It is noted that discriminant analysis is closely related to the understanding of “non-recognition”, which means that it is not classified with the separating function. If necessary, the classification should be carried out by other means (for example, manually). It is shown that the use of classification abandonment can dramatically improve the quality of classification, although the number of correct classification decisions is decreasing. Estimation techniques of the separating function on the basis of its classification error estimation are stated. The simplest possibility is to apply it to the objects under training and count the number of incorrect decisions. It is proved that the application of the technique results in an “optimal” offset. The possibility for separating multiple objects into training and control ones (the technique of concealment of available information) is shown. The training set is used to form the separating function, and the control set is used to estimate its quality. It is proved that the application of the technique results in a “pessimistic” offset of the result related to the separating function being formed on the basis of the full set of objects. The correlation between the techniques for the control of the training set is proposed. It is the technique for exclusion from the training set and application of the training set as a control single element. The proposed technique involves removing only one object from the training set, which is then used to estimate the quality of the separating function.
Compact Fenwick trees for dynamic ranking and selection

Summary The Fenwick tree is a classical implicit data structure that stores an array in such a way that modifying an element, accessing an element, computing a prefix sum and performing a predecessor search on prefix sums all take logarithmic time. We introduce a number of variants which improve the classical implementation of the tree: in particular, we can reduce its size when an upper bound on the array element is known, and we can perform much faster predecessor searches. Our aim is to use our variants to implement an efficient dynamic bit vector : our structure is able to perform updates, ranking and selection in logarithmic time, with a space overhead in the order of a few percents, outperforming existing data structures with the same purpose. Along the way, we highlight the pernicious interplay between the arithmetic behind the Fenwick tree and the structure of current CPU caches, suggesting simple solutions that improve performance significantly.
Design and Implementation of an English Pronunciation Scoring System for Pupils Based on DNN-HMM

Nowadays, the problem of poor performance on English pronunciation among Chinese pupils is widespread because of the shortage of foreign teachers, related products and resources. To improve their English pronunciation, we have developed an English word pronunciation scoring system. The system applies DNN-HMMs (deep neural network-hidden Markov models) for acoustic modeling to implement the English speech recognition and the GOP (Goodness of Pronunciation) algorithm for scoring pronunciation based on the likelihood output by DNN-HMMs. The method is implemented on HTK (Hidden Markov Model Toolkit), which integrates a set of tools for building speech recognition system. Moreover, the system is practically deployed, in which the server side conducts the model training; the client side collects pupils' speech data, and feedbacks pupils the inferred scores. Specifically, we collect 150 word pronunciations of 30 pupils as test data and compare the scores given by the system with scores given by teachers. The results demonstrate the reliability of the developed pupil pronunciation scoring system based on DNN-HMM.
A New Method for Format Preserving Encryption in High-Data Rate Communications

In some encryption systems it is necessary to preserve the format and length of the encrypted data. This kind of encryption is called FPE (Format Preserving Encryption). Currently, only two AES (Advanced Encryption Standard) modes of operation recommended by the NIST (National Institute of Standards and Technology) are able to implement FPE algorithms, FF1 and FF3. These modes work in an electronic codebook fashion and can be configured to encrypt databases with an arbitrary format and length. However, there are no stream cipher proposals able to implement FPE encryption for high data rate information flows. The main novelty of this work is a new block cipher operation mode proposal to implement an FPE algorithm in a stream cipher fashion. It has been called CTR-MOD and it is based on a standard block cipher working in CTR (Counter) mode and a modulo operation. The confidentiality of this mode is analyzed in terms of its IND- CPA (Indistinguishability under Chosen Plaintext Attack) advantage of any adversary attacking it. Moreover, the encryption scheme has been implemented on an FPGA (Field Programmable Gate Array) and has been integrated in a Gigabit Ethernet interface to test an encrypted optical link with a real high data rate traffic flow.
Statistically consistent and computationally efficient inference of ancestral DNA sequences in the TKF91 model under dense taxon sampling

In evolutionary biology, the speciation history of living organisms is represented graphically by a phylogeny, that is, a rooted tree whose leaves correspond to current species and whose branchings indicate past speciation events. Phylogenetic analyses often rely on molecular sequences, such as DNA sequences, collected from the species of interest, and it is common in this context to employ statistical approaches based on stochastic models of sequence evolution on a tree. For tractability, such models necessarily make simplifying assumptions about the evolutionary mechanisms involved. In particular, commonly omitted are insertions and deletions of nucleotides—also known as indels. Properly accounting for indels in statistical phylogenetic analyses remains a major challenge in computational evolutionary biology. Here, we consider the problem of reconstructing ancestral sequences on a known phylogeny in a model of sequence evolution incorporating nucleotide substitutions, insertions and deletions, specifically the classical TKF91 process. We focus on the case of dense phylogenies of bounded height, which we refer to as the taxon-rich setting, where statistical consistency is achievable. We give the first explicit reconstruction algorithm with provable guarantees under constant rates of mutation. Our algorithm succeeds when the phylogeny satisfies the “big bang” condition, a necessary and sufficient condition for statistical consistency in this setting.
Transfer Learning for Detecting Hateful Sentiments in Code Switched Language

With the phenomenal increase in the penetration of social media in linguistically diverse demographic regions, conversations have become more casual and multilingual. The rise of informal code-switched multilingual languages makes it tough for automated systems to monitor instances of hate speech, which are further intelligently disguised through the use of spelling variations, code-mixing, homophones, homonyms, and the absence of sophisticated grammar rules. Machine transliteration can be employed for converting the code-switched text into a singular script but poses the challenge of the semantical breakdown of the text. To overcome this drawback, this chapter investigates the application of transfer learning. The CNN-based neural models are trained on a large dataset of hateful tweets in a chosen primary language, followed by retraining on the small transliterated dataset in the same language. Since transfer learning can act as an effective strategy to reuse already learned features in learning a specialized task through cross-domain knowledge transfer, hate speech classification on a large English corpus can act as source tasks to help in obtaining pre-trained deep learning classifiers for the target task of classifying tweets translated in English from other code-switched languages. Effects of the different types of popular word embeddings and multiple supervised inputs such as the LIWC, the presence of profanities, and sentiment are carefully studied to derive the most representative combination of input settings that can help achieve state-of-the-art hate speech detection from code-switched multilingual short texts on Twitter.
Subspace Gaussian mixture based language modeling for large vocabulary continuous speech recognition

This paper focuses on adaptable continuous space language modeling approach of combining longer context information of recurrent neural network (RNN) with adaptation ability of subspace Gaussian mixture model (SGMM) which has been widely used in acoustic modeling for automatic speech recognition (ASR). In large vocabulary continuous speech recognition (LVCSR) it is a challenging problem to construct language models that can capture the longer context information of words and ensure generalization and adaptation ability. Recently, language modeling based on RNN and its variants have been broadly studied in this field. The goal of our approach is to obtain the history feature vectors of a word with longer context information and model every word by subspace Gaussian mixture model such as Tandem system used in acoustic modeling for ASR. Also, it is to apply fMLLR adaptation method, which is widely used in SGMM based acoustic modeling, for adaptation of subspace Gaussian mixture based language model (SGMLM). After fMLLR adaptation, SGMLMs based on Top-Down and Bottom-Up obtain WERs of 5.70 % and 6.01%, which are better than 4.15% and 4.61% of that without adaptation, respectively. Also, with fMLLR adaptation, Top-Down and Bottom-Up based SGMLMs yield absolute word error rate reduction of 1.48%, 1.02% and a relative perplexity reduction of 10.02%, 6.46% compared to RNNLM without adaptation, respectively.
Proposed software faults detection using hybrid approach

Abstract The major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naïve Bayes, and k‐nearest neighbor for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect‐free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or nondefective modules in a dataset. This paper presents an empirical study on National Aeronautics and Space Administration Metric Data Program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods.
Increasing Perceived Agency in Human‐AI Interactions: Learnings from Piloting a Voice User Interface with Drivers on Uber

This case study seeks to increase understanding of how agency is fostered in human‐AI interaction by providing insight from Uber's development of a conversational voice‐user‐interface (VUI) for its driver application. Additionally, it provides user researchers with insight on how to identify agency's importance early in the product development process and communicate it effectively to product stakeholders. First, the case reviews the literature to provide a firm theoretical basis of agency. It then describes the implementation of a novel in‐car Wizard‐Of‐Oz study and its usefulness in identifying agency as a critical mediator of driver interaction with the VUI before software‐development. Afterward, three factors which impacted driver agency and product usage are discussed – conversational agency, use of the VUI in social contexts and perception of the VUI persona. Finally, the case describes strategies used to convince the engineering and product teams to prioritize features to increase agency. As a result, the findings led to substantive changes to the VUI to increase agency and enhance the user experience.
NLP chatbot for Discharge Summaries

One of the most important goals in health-care is improving the quality of health and service rendered by healthcare industry. There has been a growing demand for ease of access to health-care, and this necessitates the development of health-care technology with Human Computer Interaction(HCI) design in mind. HCI usage can help both patients and healthcare providers as well [3]. With a wealth of information available to patients and providers, it can be difficult to find relevant information in a timely manner, especially for providers with busy schedules and patients with limited health literacy. We propose a Human Computer Interaction system that can answer patientś question and help improve the quality of health-care services provided to an individual. We make use of Natural Language Processing, Machine Learning and Big Data technologies to provide a feasible deploy-able solution. for the same.
Construction of Space Object Situation Information Service Based on Knowledge Graph

To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multi-element knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users' cognition of SOS knowledge.
Multi-tier Edge-to-Cloud Architecture for Adaptive Video Delivery

In the last few years, there has been a rapid proliferation of a wide range of real-time video services and applications. These technologies flood the wireless systems with video content on a daily basis. As a result of this sharp increase in video traffic, the prospect of errors due to network interference and congestion rises. Incidentally, the adoption of the 5th generation of wireless systems (5G) will allow this growth to be even greater due to its high bandwidth capacity and low latency. However, even with these improvements on the wireless capabilities, a reliable and high-quality video transmission still imposes several challenges, such as how to handle a large number of heterogeneous devices and how to better use the resource-richer Edge, Fog, and Cloud computing sources to meet the user's requirements. To overcome these issues, this work proposes a multi-tier video delivery architecture relying upon several technologies such as Multi-access Edge computing (MEC), 5G slices, and microservice placement/chaining. Furthermore, to assess the proposed idea an experimental proof-of-concept testbed of the multi-tier architecture was designed, implemented, and evaluated using real-world tools and actual video sequences. The results obtained supported our claim that a multi-tier video delivery system is feasible and can greatly benefit the end-users.