Reviewerly/exHarmony huggingface dataset:
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling

In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation. The key insight we show in this paper is that speed and accuracy are not necessarily a trade-off when it comes to image scaling. Our results show that re-scaling the image to a lower resolution will sometimes produce better accuracy. Based on this observation, we propose a novel approach, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection. To this end, our results on ImageNet VID and mini YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP improvement with 1.6x and 1.8x speedup, respectively. Additionally, we improve state-of-the-art video acceleration work by an extra 1.25x speedup with slightly better mAP on ImageNet VID dataset.
Accurate and Robust Rogue Access Point Detection with Client-Agnostic Wireless Fingerprinting

The broadcast nature of wireless medium makes WLANs easily be attacked by rogue Access Points (APs). Rogue AP attacks can potentially cause severe privacy leakage and financial lost. Hardware fingerprinting is the state-of-the-art technology to detect rogue APs, since an attacker would find it difficult to set up a rogue AP with specific hardware fingerprints. However, existing hardware fingerprints not only depend on the AP, but also depend on the client, significantly limiting their applicable scenarios. In this work, we investigate two novel client-agnostic fingerprints, which can be extracted using commercial off-the-shelf WiFi devices, to detect rogue APs. One is the power amplifier non-linearity fingerprint and the other is the frame interval distribution fingerprint. These two fingerprints remain consistent over time and space for the same AP but vary across different APs even with the same brand, model and firmware. We use the fingerprint similarity between the candidate AP and the authorized AP for device authentication. Our scheme can be implemented without modifying the infrastructural APs and can work well with new clients without rebuilding the fingerprint database. We evaluate our scheme in both in-lab and field scenarios, by analyzing 12 million WiFi packets. Results shows that our scheme achieves an overall 96.55% positive detection rate and a 4.31% false alarm rate.
The Effect of T-Ball Class on Physical Self-Efficacy of Elementary School Students Using Virtual Reality Technology(VR)

The purpose of this study was to verify the effectiveness of virtual reality(VR) contents as a means to improve the quality of physical education by analyzing the effects of T-ball lesson using VR technology on physical self-efficacy of elementary school students. Participants were selected as 90 students in 6th grade A elementary school in Gyeonggi-do, where VR sports room is introduced, and 90 6th grade students in B elementary school running traditional physical education classes. After conducting the pre-test, the experiment group conducted a T-ball class using VR technology over a total of five times, and the control group conducted T-ball lessons in the playground. After the experimental treatments were completed, a post-test was conducted. The independent variables in this study were test, teaching method, and gender, and the dependent variables are physical self-efficacy. The collected data was analyzed by performing a 3-way (2x2x2) ANOVA using the SPSS 23.0 statistical program. As a result of data analysis, the T-Ball class using VR Technology had a positive effect on perceived physical ability factors[F (1,176)=25.899, p＜.001] and physical self-expression confidence factors[F (1,176)=15.059, p＜.001]. These results are believed to have influenced the environmental characteristics of the VR sports room, such as difficulty control, information and data presented on the screen, 3D-based three-dimensional educational environment, and smooth communication. In the future, it is necessary to develop and disseminate various virtual reality sports contents and conduct research on various grades.
NET-LDA: a novel topic modeling method based on semantic document similarity

Topic models, such as latent Dirichlet allocation (LDA), allow us to categorize each document based on the topics. It builds a document as a mixture of topics and a topic is modeled as a probability distribution over words. However, the key drawback of the traditional topic model is that it cannot handle the semantic knowledge hidden in the documents. Therefore, semantically related, coherent and meaningful topics cannot be obtained. However, semantic inference plays a significant role in topic modeling as well as in other text mining tasks. In this paper, in order to tackle this problem, a novel NET-LDA model is proposed. In NET-LDA, semantically similar documents are merged to bring all semantically related words together and the obtained semantic similarity knowledge is incorporated into the model with a new adaptive semantic parameter. The motivation of the study is to reveal the impact of semantic knowledge in the topic model researches. Therefore, in a given corpus, different documents may contain different words but may speak about the same topic. For such documents to be correctly identified, the feature space of the documents must be elaborated with more powerful features. In order to accomplish this goal, the semantic space of documents is constructed with concepts and named entities. Two datasets in the English and Turkish languages and 12 different domains have been evaluated to show the independence of the model from both language and domain. The proposed NET-LDA, compared to the baselines, outperforms in terms of topic coherence, F-measure, and qualitative evaluation.
Random two-frame interferometry based on deep learning

A two-frame phase-shifting interferometric wavefront reconstruction method based on deep learning is proposed. By learning from a large number of simulation data based on a physical model, the wrapped phase can be calculated accurately from two interferograms with an unknown phase step. The phase step can be any value excluding the integral multiples of π and the size of interferograms can be flexible. This method does not need a pre-filtering to subtract the direct-current term, but only needs a simple normalization. Comparing with other two-frame methods in both simulations and experiments, the proposed method can achieve better performance.
Experimental and numerical analysis of 3D printed open-hole plates reinforced with carbon fibers

The present work investigates the possibility of reducing the strain/stress concentrations in a open-hole plate using localized 3D printed carbon fiber reinforcements. Several reinforcement strategies have been investigated exploiting the capabilities of a recent additive manufacturing process, the carbon filament fabrication, that allows continuous carbon fibers to be arbitrarily oriented on a plate. Both experimental and numerical investigations have been performed to evaluate the effectiveness of the reinforcements in the mitigation of the strain/stress concentration. The strain fields evaluated during the experimental test, acquired using a digital image correlation technique, have been compared with the results coming from a finite element model. The reliability of the finite element model has been evaluated and its capability to predict the behavior of such complex structures has been checked. The results have shown that the use of an appropriate reinforcement strategy can lead to a considerable reduction in the strain/stress concentration and that an appropriate finite element model can be used to investigate such structures.
A New Method for Analyzing the Performance of the Harmony Search Algorithm

A harmony search (HS) algorithm for solving high-dimensional multimodal optimization problems (named DIHS) was proposed in 2015 and showed good performance, in which a dynamic-dimensionality-reduction strategy is employed to maintain a high update success rate of harmony memory (HM). However, an extreme assumption was adopted in the DIHS that is not reasonable, and its analysis for the update success rate is not sufficiently accurate. In this study, we reanalyzed the update success rate of HS and now present a more valid method for analyzing the update success rate of HS. In the new analysis, take-k and take-all strategies that are employed to generate new solutions are compared to the update success rate, and the average convergence rate of algorithms is also analyzed. The experimental results demonstrate that the HS based on the take-k strategy is efficient and effective at solving some complex high-dimensional optimization problems.
AugmenTech: The Usability Evaluation of an AR System for Maintenance in Industry

Maintenance tasks are common operations in manufacturing environments. However, it is becoming increasingly challenging for companies to recruit personnel with adequate skills and tacit knowledge to perform them. This paper proposes the use of augmented reality (AR) as a solution to provide contextually relevant guidance as a method to upskill a user performing a task. However, implementing a disruptive tool such as AR into the workforce requires considerations for acceptability and compliance amongst employees. AugmenTech is a proposed AR guidance application which was designed and tested with a strong focus on usability. Due to issues with the HoloLens native input modalities in the application area, a tactile input module has been designed and integrated with the system. A mock-up bearing replacement maintenance procedure, based on a real-world use case provided by a local manufacturer, has been designed as a platform for evaluation. Using this task and AugmenTech a between-subjects experiment was conducted, comparing the system with a traditional paper documentation approach. Overall workload, quantified using the NASA-TLX, was similar when using AR. Likewise, the use of AR also had minimal effect on time needed to complete the task. The usability of AugmenTech has been quantified, scoring highly, (M = 79.5, SD = 16.13), using the System Usability Scale (SUS) questionnaire.
Aspect-based classification of product reviews using Hadoop framework

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Vertex nomination, consistent estimation, and adversarial modification

Given a pair of graphs G 1 and G 2 and a vertex set of interest in G 1 , the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in G 2 (if they exist) and produce a rank list of the vertices in G 2 , with the corresponding vertices of interest in G 2 concentrating, ideally, at the top of the rank list.In this paper, we define and derive the analogue of Bayes optimality for VN with multiple vertices of interest, and we define the notion of maximal consistency classes in vertex nomination.This theory forms the foundation for a novel VN adversarial contamination model, and we demonstrate with real and simulated data that there are VN schemes that perform effectively in the uncontaminated setting, and adversarial network contamination adversely impacts the performance of our VN scheme.We further define a network regularization method for mitigating the impact of the adversarial contamination, and we demonstrate the effectiveness of regularization in both real and synthetic data.
Understanding and Detecting Dangerous Speech in Social Media

Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60% macro F1, significantly outperforming a competitive baseline.
Cluster-Based Classification of Blockchain Consensus Algorithms

In recent years, Blockchain has become a disruptivetechnology to protect the integrity of information, especially inopen and collaborative information systems. Its main advantageis the possibility to reach consensus on the new data blocks tobe added to the chain, even with anonymous actors. The mostcommon consensus mechanism is Proof of Work, but it has beenproven to be very inefficient in terms of energy spent by themembers of the blockchain. In the literature there are many othertechniques that pretend to become the new popular mechanism.However, the number of this techniques is growing too fast toreally be able to differentiate among all the options. In this work,a new characterization of consensus algorithm is proposed, thatcan be used to find families of mechanism using cluster-basedclassification. Using the Ward Method and Spearmans RankCorrelation analysis, new clusters of consensus mechanisms wereidentified. The results describe the behavioral patterns not seenbefore in the literature. In addition, some open problems ofcurrent consensus algorithms are discussed.
Empirical Analysis of Bitcoin network (2016-2020)

Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence. Bitcoin transaction data from 01 Jan 2016 00:00:00 GMT to 08 May 2020 13:21:33 GMT was processed for this purpose to build a Bitcoin user graph.
Cloud Forensics: Challenges and Blockchain Based Solutions

With the advancement in digital forensics, digital forensics has been evolved in Cloud computing.A common process of digital forensics mainly includes five steps: defining problem scenario, collection of the related data, investigation of the crime scenes, analysis of evidences and case documentation.The conduction of digital forensics in cloud results in several challenges, security, and privacy issues.In this paper, several digital forensics approaches in the context of IoT and cloud have been presented.The review focused on zone-based approach for IoT digital forensics where the forensics process is divided into three zones.Digital forensics in cloud provides the facilities of large data storage, computational capabilities and identification of criminal activities required for investigating forensics.We have presented a brief study on several issues and challenges raised in each phase of Cloud forensics process.The solution approaches as well as advancement prospects of cloud forensics have been described in the light of Blockchain technology.These studies will broaden the way to new researchers for better understanding and devising new ideas for combating the challenges.
Evaluation of the usefulness of path of long-term user experience model in design process

User experience evolves in accordance with users’ attitudes, habits, and personality, as well as their prior experiences. Even though there are attempts to holistically frame the long-term experience of interactive products, there is still debate on how to guide designers in the design process of those products. To tackle this issue, this paper proposes the usage of Path of Long-Term User Experience (PLUX) model in design process. To interrogate the usefulness of the model, we conducted two studies. First, we adopted, adapted and tested the applicability of the backcasting approach, together with the visual materials that could be used in the design process. Following this, we tested the effectiveness of using the PLUX model as a guidance tool in designing interactive products. The PLUX model together with the backcasting approach helps designers to better ground their decisions in the design process and to find ways to enrich long-term user experience. The stages of user experience as well as the human and product-related qualities of the PLUX model facilitated in-depth reflection, and espoused creative interpretations of these qualities for ideation. However, the high level of detail is found limiting at times, depending on the ways the model is utilised.
Using the Crowd to Prevent Harmful AI Behavior

To prevent harmful AI behavior, people need to specify constraints that forbid undesirable actions. Unfortunately, this is a complex task, since writing rules that distinguish harmful from non-harmful actions tends to be quite difficult in real-world situations. Therefore, such decisions have historically been made by a small group of powerful AI companies and developers, with limited community input. In this paper, we study how to enable a crowd of non-AI experts to work together to communicate high-quality, reliable constraints to AI systems. We first focus on understanding how humans reason about temporal dynamics in the context of AI behavior, finding through experiments on a novel game-based testbed that participants tend to adopt a long-term notion of harm, even in uncertain situations that do not affect them directly. Building off of this insight, we explore task design for long-term constraint specification, developing new filtering approaches and new methods of promoting user reflection. Next, we develop a novel rule-based interface which allows people to craft rules in an accessible fashion without programming knowledge. We test our approaches on a real-world AI problem in the domain of education, and find that our new filtering mechanisms and interfaces significantly improve constraint quality and human efficiency. We also demonstrate how these systems can be applied to other real-world AI problems (e.g. in social networks).
Smart Sensors and Devices in Artificial Intelligence

As stated in the Special Issue call, "sensors are eyes or/and ears of an intelligent system, such as Unmanned Aerial Vehicle (UAV), Automated Guided Vehicle (AGV) and robots [...].
Combination of End-to-End and Hybrid Models for Speech Recognition

Recent studies suggest that it may now be possible to construct end-to-end Neural Network (NN) models that perform on-par with, or even outperform, hybrid models in speech recognition. These models differ in their designs, and as such, may exhibit diverse and complementary error patterns. A combination between the predictions of these models may therefore yield significant gains. This paper studies the feasibility of performing hypothesis-level combination between hybrid and end-to-end NN models. The end-to-end NN models often exhibit a bias in their posteriors toward short hypotheses, and this may adversely affect Minimum Bayes’ Risk (MBR) combination methods. MBR training and length normalisation can be used to reduce this bias. Models are trained on Microsoft’s 75 thousand hours of anonymised data and evaluated on test sets with 1.8 million words. The results show that significant gains can be obtained by combining the hypotheses of hybrid and end-to-end NN models together.
FedSmart: An Auto Updating Federated Learning Optimization Mechanism

Federated learning has made an important contribution to data privacy-preserving. Many previous works are based on the assumption that the data are independently identically distributed (IID). As a result, the model performance on non-identically independently distributed (non-IID) data is beyond expectation, which is the concrete situation. Some existing methods of ensuring the model robustness on non-IID data, like the data-sharing strategy or pre-training, may lead to privacy leaking. In addition, there exist some participants who try to poison the model with low-quality data. In this paper, a performance-based parameter return method for optimization is introduced, we term it FederatedSmart (FedSmart). It optimizes different model for each client through sharing global gradients, and it extracts the data from each client as a local validation set, and the accuracy that model achieves in round t determines the weights of the next round. The experiment results show that FedSmart enables the participants to allocate a greater weight to the ones with similar data distribution.
Robust-fidelity hyperparallel controlled-phase-flip gate through microcavities

Hyperparallel quantum information processing outperforms its traditional parallel one in terms of channel capacity, low loss rate, and processing speed. We present a way for implementing a robust hyperparallel optical controlled-phase-flip gate through microcavities. The gate acts on polarization and spatial degrees of freedom simultaneously, and the incomplete and undesired interactions between photons and quantum dots are prevented. Interestingly, the unity-fidelity of the gate can be achieved in principle, and the success of the gate is heralded by the single-photon detectors.
Association rules extraction for the identification of functional dependencies in complex technical infrastructures

This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).
Session-based Hotel Recommendations Dataset

In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe is vitally important for recommendation systems research in the area of hotel search, from both academic and industry perspectives. In this article, we describe the qualitative characteristics of the dataset and present the comparison of several baseline algorithms trained on the data.