Reviewerly/exHarmony huggingface dataset:
The Use of Immersive Technologies for Concept Design

In this paper, a method is proposed for using virtual reality for concept design with the Gravity Sketch VR application and a virtual reality headset. The method for augmented concept design is then tested against traditional sketching on paper. The participants were thirteen product development master students, challenged to design backpacks in VR and on paper. Different parameters like efficiency, ease of use and enjoyment were measured through observation, a survey and a laddering interview. Even though the master students had no experience with virtual reality, the VR method scored surprisingly high on efficiency, effectiveness and ease of use. The participants specified that they enjoyed working with VR more, however none of them were likely to completely replace their traditional sketching methods. Nevertheless, this study supports the claim that virtual reality technology is ready for product design.
Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window

Electricity consumption and coal consumption are two important indicators in the cement calcination process. Modeling predictions of cement energy consumption support efforts aimed at understanding energy use and energy conservation. However, due to the three characteristics of cement: time-varying delay, non-linearity and uncertainty, it is very difficult to establish accurate energy consumption prediction models. To solve the above problems, a multiple-index energy consumption prediction model based on sliding window deep belief network (SW-DBN) is proposed in this paper. Specifically, to avoid studying complex problem of time-varying delay, the sliding window method is introduced to deep belief network, which combines the previous and current variable data into time series data. As a result, all temporal information related to the energy consumption data is fed to the input layer of deep belief network. Then deep belief network is utilized to establish the multiple-index energy consumption prediction model on the temporal information, which is capable of predicting electricity consumption and coal consumption simultaneously. Experimental results show that the proposed model obtains improvement for multiple-index energy consumption prediction model in cement calcination process.
Effectiveness of Kotlin vs. Java in android app development tasks

Context:Kotlin is a new programming language representing an alternative to Java; they both target the same JVM and can safely coexist in the same application. Kotlin is advertised as capable to solve several known limitations of Java. Recent surveys show that Kotlin achieved a relevant diffusion among Java developers. Goal:We planned to empirically assess a few typical promises of Kotlin w.r.t. known Java’s limitations, in terms of development effectiveness, maintainability, and ease of development. Method:Our experiment involved 27 teams of 4 people each that completed a set of maintenance tasks (both defect correction and feature addition) on Android apps written in either Java or Kotlin. In addition to the number of fixed defects, effort, and code size, we collected, though a questionnaire, the participants’ perceptions about the avoidance of known pitfalls. Results:We did not observe any significant difference in terms of maintainability between the two languages.We found a significant difference regarding the amount of code written, which constitutes evidence of better conciseness of Kotlin. Concerning ease of development, the frequency of NullPointerExceptions reported by the subjects was significantly lower when developing in Kotlin. On the other hand, no significant difference was found in the occurrence of other common Java pitfalls. Finally, the IDE support was deemed better for Java than Kotlin. Conclusions:Some of the promises of Kotlin to be a ”better Java” have been confirmed by our empirical assessment. Evidence suggests that the effort in transitioning to Kotlin can provide some advantages to Java developers, especially regarding code conciseness.Our results may serve as the basis for further investigations on the properties of the language.
DKEN: Deep knowledge-enhanced network for recommender systems

Despite that existing knowledge graphs embedding (KGE) based methods can achieve better recommendation performance compared with deep learning based ones, such improvement is limited due to lack of capturing the shared information between user-item interaction and item-item relation encoded in knowledge graph (KG) by fully leveraging the implicit and explicit relationship. To address this issue, in this paper, we propose a principled deep knowledge-enhanced network (DKEN) framework based on deep learning and KGE to model the semantics of entities and relations encoded in the KG. In particular, the DKEN utilizes deep neural networks (DNN) to learn higher-order feature interactions and ensembles KGE features with DNN features into an end-to-end learning process naturally to exploit implicit interaction and explicitt semantic features. Furthermore, a cross information sharing (CIS) layer is designed to facilitate information sharing between items and entities, and two aggregators are developed to improve the performance of the model. Extensive experiments on several public datasets, as well as online AB tests of an industrial recommendation scenario in the Ant Financial Service Group, demonstrate that DKEN achieves remarkably better performance than several state-of-the-art baselines.
Land Registry Using Blockchain - A Survey of existing systems and proposing a feasible solution

Storing sensitive information like property papers needs a secure and untamperable database. Existing centralized systems has numerous drawbacks like performance bottleneck and single point of failure. Hence, a decentralized system with high security is needed. To cater this need, in this paper, we propose blockchain based system for secure storage of property papers. In our system, when a land is bought by a person, the government authority will provide the person with the hard copy of property papers and our system will store the documents into the Inter Planetary File System (IPFS), a decentralised database. The IPFS network will generate the hash of document. This hash will be securely stored into the ethereum blockchain after satisfying the conditions of the smart contracts. The smart contracts will validate and verify the documents from the government authorities. This will create a decentralized, tamper-proof ledger from which we can retrieve the stored data easily.
Chinese Short Text Classification with Mutual-Attention Convolutional Neural Networks

The methods based on the combination of word-level and character-level features can effectively boost performance on Chinese short text classification. A lot of works concatenate two-level features with little processing, which leads to losing feature information. In this work, we propose a novel framework called Mutual-Attention Convolutional Neural Networks, which integrates word and character-level features without losing too much feature information. We first generate two matrices with aligned information of two-level features by multiplying word and character features with a trainable matrix. Then, we stack them as a three-dimensional tensor. Finally, we generate the integrated features using a convolutional neural network. Extensive experiments on six public datasets demonstrate improved performance of our new framework over current methods.
Discovering Graph Functional Dependencies

This article studies discovery of Graph Functional Dependencies (GFDs), a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]-hard in general. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large-scale graphs, by providing parallel scalable algorithms that guarantee to reduce running time when more processors are used. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.
A Method for Sensor-Based Activity Recognition in Missing Data Scenario

Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.
GATE: Graph-Attention Augmented Temporal Neural Network for Medication Recommendation

Medication recommendation based on Electronic Health Records (EHRs) is an important research direction, which aims to make prescription recommendations according to EHRs of patients. Most existing methods either only make recommendation through EHRs of the current admission while ignoring the patient's historical records, or fail to fully consider the correlations among the clinical events from every single admission. These methods have shown their limitations in dealing with the complex structural correlations and temporal dependencies of clinical events in EHRs, which results in the defect of recommendation quality as well as the lack of temporal prediction ability. For that, a novel graph-attention augmented temporal neural network is proposed to model both the structural and temporal information simultaneously. For each admission record, a co-occurrence graph is constructed to establish the correlations among clinical events, and then a graph-attention augmented mechanism is used to learn the structural correlations on the graphs to obtain better representation of this admission. Then a temporal updating module based on the gated recurrent units is further proposed to learn the temporal dependencies between multiple admissions of each patient. Furthermore, our proposed model is also constrained by the co-occurrence graph, which can capture the internal correlations of clinical events and provide better modeling capability when the training data is sparse. Experiments illustrate that our model is superior to the state-of-the-art methods on a real-world dataset MIMIC-III in all effectiveness measures.
Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems

Abstract The ongoing growth of use-cases for artificial neural networks (ANNs) fuels the search for new, tailor-made ANN-optimized hardware. Neuromorphic (brain-like) computers are among the proposed highly promising solutions, with optical neuromorphic realizations recently receiving increasing research interest. Among these, photonic neuronal models based on vertical cavity surface emitting lasers (VCSELs) stand out due to their favourable properties, fast operation and mature technology. In this work, we experimentally demonstrate different strategies to encode information into ultrafast spiking events in a VCSEL-neuron. We evaluate how the strength of the input perturbations (stimuli) influences the spike activation time, allowing for spike latency input coding. Based on a study of refractory behaviour in the system, we demonstrate the capability of the VCSEL-neuron to perform reliable binary-to-spike information coding with spiking rates surpassing 1 GHz. We also report experimentally on neuro-inspired spike firing rate-coding with a VCSEL-neuron, where the strength of the input perturbation (stimulus) is continuously encoded into the spiking frequency (spike firing rate). With the prospects of neuromorphic photonic systems constantly growing, we believe the reported functionalities with the ultrafast spiking VCSEL-neurons provide a reliable, multifaceted approach for interfacing photonic neuromorphic platforms with existing computation and communication systems.
Technology acceptance model: investigating students’ intentions toward adoption of immersive 360° videos for public speaking rehearsals

Given the opportunities new technologies afford students and instructors, this mixed methods, longitudinal study examined factors impacting college students' behavioral intentions for adopting virtual reality (VR) headsets and 360° video for public speaking rehearsals. Using the technology acceptance model, quantitative findings suggest that both the perceived usefulness of these technologies for rehearsing final speech assignments and VR anxiety influence students' behavioral intentions to personally adopt VR technology and 360° video or recommend its adoption to communication centers, friends, or other instructors. Qualitatively, students discussed perceived usefulness, as well as positive and negative emotional reactions, which may act as additional facilitators and inhibitors to technology adoption. Based on our findings, conclusions and implications are suggested for those interested in adopting VR headsets and 360° video for public speaking rehearsals.
A Hybrid Neural Network for Fast Automatic Modulation Classification

Automatic modulation classification (AMC) plays a key role in cognitive radio.For AMC, convolutional neural networks (CNNs) have been explored in previous works extensively and deliver the best performance.However, temporal dependencies of signals modeled by CNNs are inherently implicit and insufficient.As a result, models need more data to learn discriminative features automatically.In this work, we propose a hybrid model named HybridNet, where a bidirectional gated recurrent unit (Bi-GRU) is placed after CNN to capture temporal dependencies explicitly.In addition, we investigate why varying Signalto-Noise Ratio (SNR) dataset makes performance deteriorate.By visualization, we discover that the increase of the intra-class divergence under sharply varying SNR is the central cause.To this end, channel-wise attention is adopted in HybridNet to learn different patterns existing in SNR, which does not require SNR labels in the training process or inference values of SNR.On RadioML2016.10b,our HybridNet obtains the best accuracy among all scales of training data.Especially, in small datasets, our model obtains 87.4% accuracy that is 9.7% higher than the baseline method.
Quantum-like influence diagrams for decision-making

This article proposes a novel and comprehensive framework on how to describe the probabilistic nature of decision-making process. We suggest extending the quantum-like Bayesian network formalism to incorporate the notion of maximum expected utility to model human paradoxical, sub-optimal and irrational decisions. What distinguishes this work is that we take advantage of the quantum interference effects produced in quantum-like Bayesian Networks during the inference process to influence the probabilities used to compute the maximum expected utility of some decision. The proposed quantum-like decision model is able to (1) predict the probability distributions found in different experiments reported in the literature by modelling uncertainty through quantum interference, (2) to identify decisions that the decision-makers perceive to be optimal within their belief space, but that are actually irrational with respect to expected utility theory, (3) gain an understanding of how the decision-maker's beliefs evolve within a decision-making scenario. The proposed model has the potential to provide new insights in decision science, as well as having direct implications for decision support systems that deal with human data, such as in the fields of economics, finance, psychology, etc.
Active learning for hierarchical multi-label classification

Due to technological advances, a massive amount of data is produced daily, presenting challenges for application areas where data needs to be labelled by a domain specialist or by expensive procedures, in order to be useful for supervised machine learning purposes. In order to select which data points will provide more information when labelled, one can make use of active learning methods. Active learning (AL) is a subfield of machine learning which addresses methods to build models with fewer, but more representative instances. Even though AL has been vastly studied, it has not been thoroughly investigated in hierarchical multi-label classification, a learning task where multiple class labels can be assigned to an instance and these labels are hierarchically structured. In this work, we provide a public framework containing baseline and state-of-the-art algorithms suitable for this task. Additionally, we also propose a new algorithm, namely Hierarchical Query-By-Committee (H-QBC), which is validated on datasets from different domains. Our results show that H-QBC is capable of providing superior predictive performance results compared to its competitors, while being computationally efficient and parameter free.
Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations

To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.
Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit ( https://www.korbit.ai ), a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
A novel chaotic encryption scheme based on efficient secret keys and confusion technique for confidential of DICOM images

This paper proposed a secure encryption scheme based on the chaotic system and periodic confusion strategy with dynamic secret keys to protect medical images. The proposed scheme can be applied to DICOM standard images which are generally employed in medical systems and applications. Firstly, secret keys are extract from the pixels' information of the medical image. Then, the positions of pixels are permuted based on periodic confusion strategy and pseudo-random sequences. Finally, permuted image pixels are coded based on sequences of logistic systems and the XOR operator. The performance of the presented encryption scheme was evaluated by several security analyses. The results assure the efficiency of the proposed schema, the NPCR and UACI are improved over 99.6% and 33.5% respectively, and entropy is reported close to 7.9994. The comparison with similar DICOM encryption algorithms is made. The analysis results confirm that our proposed encryption scheme meets the security requirements of medical images encryption and is capable of resisting the main cryptographic attacks.
New approach for threat classification and security risk estimations based on security event management

Security Information and Event Management (SIEM) systems are essential for identifying cyber attacks, being an extended practice in organizations to detect threats, vulnerabilities and to estimate security risks. The management of events and information related to security is done through systems that provide all the information, processing different data sources. The developing of alternative models that provide complementary information to commercial solutions, based on the same data sources, is presented as a novel and interesting challenge, not only for organizations, but also for the scientific community. This paper presents a new system to classify security threats, computing their criticality according to the Bug Bar technique, with the aim of addressing threats in order of priority. High correlations were achieved between severity risk values achieved from commercial systems and results computed by the new approach. Accordingly, the new proposal could complement the information of SIEM systems, and help in the prediction of criticalities of future threats.