Reviewerly/exHarmony huggingface dataset:
Neural Network Guided Perceptually Optimized Bit-Allocation for Block-Based Image and Video Compression

Bit-allocation based on the MSE is computationally convenient in image and video compression, but leads to perceptually suboptimal compression results. Distortion sensitivity, modeled as a reference specific property, can be used to improve the accuracy of perceptual quality prediction based on the MSE. This paper shows how distortion sensitivity directly leads to computationally beneficial perceptual optimization of irrelevance reduction and, thereby, of bit-allocation in image and video compression. To this end distortion sensitivity is estimated using a deep convolutional neural network. The proposed method of distortion sensitive bit-allocation is evaluated experimentally using HEVC and on our testset shows average bit-rate reductions with regard to the MOS of 15.9% compared to constant QP-based bit-allocation and 7.3% compared to state-of-the-art perceptual bit-allocation schemes.
Emotions and language in advertising

Abstract It is widely acknowledged that advertisers employ primarily two types of persuasion strategies—rational vs. emotional—to persuade consumers to buy their products. While a rational appeal is driven by information processing by the conscious mind, in contrast, an emotional appeal is propelled by the unconscious mind or gut‐feeling(s), which has its own rewards and risks. The main goals of this paper are two‐fold: (1) to present a mechanism of encoding and decoding emotions rooted in the language; and (2) to identify linguistic markers and strategies of an emotional ad. In addition to imagery, the role of language in emotional advertising is undisputed. A linguistic analysis of monolingual as well as bilingual/multilingual ads is performed to show how language regulates desired emotional effects, identities, and appeals.
An Online Algorithm for Selling Your Reserved IaaS Instances in Amazon EC2 Marketplace

In cloud platforms such as Amazon EC2, users can reserve IaaS instances rather than buy on-demand ones to save cost. But it would incur the waste of reservations if there are few demands arriving after reserving instances. Currently, there is a reserved instance marketplace launched by Amazon EC2 cloud, where users can sell their unused instances for avoiding such waste of unused reservations. But for users, it is difficult to make the decision to sell their instances optimally without knowing any information for future demands, for it would incur the extra cost when there are new demands arriving after selling their reservations. For solving this problem, an online selling algorithm is proposed in this paper to guide cloud users in selling reserved instances in Amazon EC2 marketplace. We prove theoretically that our online algorithm Aβ can guarantee a bounded competitive ratio of 2T/β, whose value is specific to the type of reserved instances. Taking the i3.large instance provided by Amazon EC2 as an example, the competitive ratio is 3.36 under its pricing rules for 1-year term. Finally, via extensive experiments using workload data collected from actual applications, we verify our online algorithm's effectiveness and demonstrate that it is much more cost effective to cloud users in IaaS platforms.
Quantum-Inspired Density Matrix Encoder for Sexual Harassment Personal Stories Classification

Nowadays, more and more sexual harassment personal stories have been shared on social media. To better monitor and analyze the extent of sexual harassment based on these social media data, we need to automatically categorize different forms of sexual harassment personal stories. Existing methods apply convolutional neural network (CNN) with different convolution window sizes to this text classification task. However, the previous CNN models do not provide an effective way to synthesize window size-related local representations, but simply concatenate all local representations together. To address this problem, we propose a new density matrix encoder, inspired by quantum mechanics, to encode local representations as particles in quantum state and generate a global representation as quantum mixed system for each story. Experiment on SafeCity dataset shows that our model outperforms CNN baseline and achieves better performance than the state-of-the-art model when considering both accuracy and speed, demonstrating the effectiveness of the proposed density matrix encoder.
Automatic detection of architectural bad smells through semantic representation of code

Bad design decisions in software development can progressively affect the internal quality of a software system, causing architecture erosion. Such bad decisions are called Architectural Smells (AS) and should be detected as soon as possible, because their presence heavily hinders the maintainability and evolvability of the software. Many detection approaches rely on software analysis techniques which inspect the structure of the system under analysis and check with rules the presence of AS. However, some recent approaches leverage natural language processing techniques to recover semantic information from the system. This kind of information is useful to detect AS which violate "conceptual" design principles, such as the separation of concerns one. In this research study, I propose two detection strategies for AS detection based on code2vec, a neural model which is able to predict semantic properties of given snippets of code.
Research on privacy enhancement scheme of blockchain transactions

Abstract Blockchain is the supporting technology for cryptocurrencies, which can be used to record transaction information among users. However, the information on blockchain can be accessed and verified by any user. To some extent, it is effortless to acquire users' identities, transaction privacy information by means of transaction tracing technology and data analysis technology. In order to tackle the above problems, we proposed a transaction privacy enhancement scheme based on the computational Diffie‐Hellman problem with provable security. By leveraging a group signature scheme, the mixing peer can mix the transactions so that the transaction sender can be hidden into a mixing group. In addition, our solution improves the efficiency by utilizing a signature aggregation scheme and reducing the overhead of the signature verification. The security analysis shows that the proposed scheme is superior in privacy and the efficiency of signature verification.
Developing a virtual reality game for manufacturing education

This paper describes the development of a virtual reality (VR) simulation game for educating engineering students in manufacturing. Undergraduate engineering students work on solving design and manufacturing problems and utilize professional skills to improve their overall performance and outcome. This research utilizes interactive and immersive VR technologies to develop a new integrated approach to understand problem solving in engineering students and determine if engineering students are attending to the correct elements while they are solving design and manufacturing problems. Furthermore, a player modeling technique is proposed to deliver an experience adapted for each student. The approach will be tested, validated and used to bridge the skills gap that exists for many undergraduate engineering students.
Incremental Graph Processing for On-line Analytics

Modern data generation is enormous; we now capture events at increasingly fine granularity, and require processing at rates approaching real-time. For graph analytics, this explosion in data volumes and processing demands has not been matched by improved algorithmic or infrastructure techniques. Instead of exploring solutions to keep up with the velocity of the generated data, most of today's systems focus on analyzing individually built historic snapshots. Modern graph analytics pipelines must evolve to become viable at massive scale, and move away from static, post-processing scenarios to support on-line analysis. This paper presents our progress towards a system that analyzes dynamic incremental graphs, responsive at single-change granularity. We present an algorithmic structure using principles of recursive updates and monotonic convergence, and a set of incremental graph algorithms that can be implemented based on this structure. We also present the required middleware to support graph analytics at fine, event-level granularity. We envision that graph topology changes are processed asynchronously, concurrently, and independently (without shared state), converging an algorithm's state (e.g. single-source shortest path distances, connectivity analysis labeling) to its deterministic answer. The expected long-term impact of this work is to enable a transition away from offline graph analytics, allowing knowledge to be extracted from networked systems in real-time.
Knowledge driven temporal activity localization

In this paper, we focus on the problem of temporal activity detection, which aims to directly predict the temporal bounds of actions. Most existing temporal activity detection algorithms treat the classification of each action proposal separately and neglect vital semantic correlations between actions in one video. This will deteriorate the classification performance in the scenario of long-tail problems, where only a handful of examples are available for uncommon actions. To solve this problem, we propose to incorporate knowledge to reason over large scale action classes and maintain semantic coherency within one video. Specifically, we employ an implicit knowledge reasoning module and an explicit knowledge reasoning module to incorporate the knowledge constraints to facilitate temporal activity localization. To demonstrate the superiority of the proposed model, we test the proposed method on large-scale action detection datasets, namely ActivityNet and THUMOS’14 datasets. The experimental results have demonstrated the superiority of the proposed model. Codes and models will be released once this paper is accepted.
A Framework to Maximise the Communicative Power of Knowledge Visualisations

Knowledge visualisation, in the field of information systems, is both a process and a product, and is informed by the closely aligned fields of information visualisation and knowledge management. Knowledge visualisation has untapped potential within the purview of knowledge communication. Even so, there is little evidence of knowledge visualisations being deployed. This might be due to a lack of evidence-based guidance to inform their creation. To improve this situation, we derived a number of "lenses" that can be used to reveal essential perspectives and feed into the visualisation production process.
Seamless Learning Design Criteria in the Context of Open and Distance Learning

Seamless learning is a form of learning whereby, regardless of location, the learning process, as it relates to learning needs and readiness, can continue through the aid of technology. Seamless learning environments are spaces that can be accessed independent of time and place through mobile or stationary devices, and that are equipped with technologies capable of meeting learning needs. With the advancements in technology, seamless learning environments are becoming increasingly popular. In this regard, the design of environments that are suitable for seamless learning in open and distance learning (ODL) fields is of critical importance. This study aimed to determine the criteria for the design of seamless learning environments in the context of ODL. In line with this aim, the Delphi technique, a qualitative research approach, was used. A total of 47 criteria under 10 different themes were identified in the study.
A socio-technical perspective to counter cyber-enabled industrial espionage

The ubiquitous digitization of information and the pervasive connectivity of work systems have inevitably facilitated cyber-enabled industrial espionage. Security failures explain most of cyber industrial espionage incidents, and insider threats represent a significant pattern in many case examples. Insiders can inadvertently or purposefully pose serious threats to organisations by facilitating access to or misuse of proprietary sensitive data. This paper argues that technical security solutions have rather limited scope to tackle this problem, and that a socio-technical approach has potential to provide a better means to address the challenge of preventing and responding to insider threats. Such an approach could bridge the gap between the design and implementation of security solutions and creation of an organisational culture that is security-aware.
A Novel Hybrid Firefly Algorithm for Global Optimization

Firefly algorithm is a new optimization technique based on swarm intelligence. It simulates the social behavior of fireflies. The search pattern of firefly algorithm is determined by the attractions among fireflies, whereby a less bright firefly moves toward a brighter firefly. In firefly algorithm, each firefly can be attracted by all other brighter fireflies in the population. But firefly algorithm is similar to other swarm intelligence algorithms; the performance of firefly algorithm is poor in high dimensional problems. It has low local search accuracy and is easy to fall into local extremum in some case. To overcome these problems, the quantum theory and mutation operation was used to improve firefly algorithm, a quantum-inspired hybrid firefly algorithm was proposed. In proposed algorithm, each quantum firefly can express two position of solution space, location update is implemented by quantum gate calculation, the mutation operation is used to jump out of the local extremum. Optimization Experiments are conducted using well-known benchmark functions. The results show that the proposed algorithm can efficiently improve the global search capability and the accuracy of solutions.
Petri Net Based Navigation Planning with Dipole Field and Dynamic Window Approach for Collision Avoidance

This paper presents a novel path planning system for multiple robots working in an uncontrolled environment in the presence of humans. The approach combines the use of Petri net to plan the movement of multiple robots to prevent the risk of congestion caused by routing several robots into a narrow region, together with a dipole field with dynamic window approach to avoid collisions of a robot with dynamic obstacles. By regarding the velocity and direction of both humans and robots as a source of magnetic dipole moment, the dipole-dipole interaction between the moving objects will generate repulsive forces to prevent collisions. The whole system is presented on robot operating system platform so that its implementation can be extendable into real robots. Experimental results with Gazebo simulator demonstrates the effectiveness of the proposed approach.
Equalization with Neural Network Circuitry for High-Speed Signal Link

Traditional data link relies on an open eye diagram to ensure correct data transfer. There are many factors, including jitter, noise, crosstalk, channel bandwidth/filtering, etc. that can shrink the eye diagram. Equalization (EQ) including the finite impulse response (FIR) EQ, continuous time linear EQ (CTLE), and decision feedback EQ (DFE) are used to improve the eye diagram. However, when the aforementioned factors are so severe that the eye is closed, data transfer will fail. We propose a new approach that does not depend on an open eye diagram. The neural network (NN) circuitry, which we call an NN interpreter, is used to replace the EQ mechanism. This new solution can ensure correct data transfer even when the eye diagram is closed with the traditional EQ mechanism. The NN interpreter takes the voltage (or current) waveform in length of several unit intervals (UIs) and make the decision of 0 or 1 based on the entire waveform instead of just comparing the voltage in the cursor UI with the reference voltage. Simulation results with a DDR5 channel show over 30% improvement in data rate with the proposed method, that is, the data rate of 6 GT/s with traditional equalization method is improved to 8 GT/s with the NN approach.
A Standard to Rule Them All: Redfish

Large data centers are complex systems that depend on several generations of hardware and software components, ranging from legacy mainframes and rack-based appliances to modular blade servers and modern rack scale design solutions. To cope with this heterogeneity, the data center manager must coordinate a multitude of tools, protocols, and standards. Currently, data center managers, standardization bodies, and hardware/software manufacturers are joining efforts to develop and promote Redfish as the main hardware management standard for data centers, and even beyond the data center. The authors hope that this article can be used as a starting point to understand how Redfish and its extensions are being targeted as the main management standard for next-generation data centers. This article describes Redfish and the recent collaborations to leverage this standard.
Composite Field Arithematic Based S-Box For AES Algorithm

Generally AES algorithm uses Substitution box which works with ROM based lookup tables. Using the rom based Look up table, there occurs a significant irreducible amount of delay in the gates as well as access paths. Different combinations of logic gates in the critical path can be employed to reduce the delay. This paper proposes new way to design a S-box which works on Composite Field arithmetic resulting in significant reduction in the area in w.r.t FPGA slices and reduction in both gate delay and combinational path delay. The proposed S-box is designed using Verilog HDL, simulated in Modelsim 6.4c and synthesized in Xilinx ISE 13.2.
Cost-Performance Trade-Offs in Fog Computing for IoT Data Processing of Social Virtual Reality

Virtual Reality (VR)-based Learning Environments (VRLEs) are gaining popularity due to the wide availability of cloud and its edge (a.k.a. fog) technologies and high-speed networks. Thus, there is a need to investigate Internet-of-Things (IoT)-based application design concepts within social VRLEs to offer scalable, cost-efficient services that adapt to dynamic cloud/fog system conditions. In this paper, we investigate the cost-performance trade-offs for an IoT-based application that integrates large-scale sensor data from Social VRLEs and coordinates the real-time data processing and visualization across cloud/fog platforms. To facilitate dynamic performance adaptation of the IoT-based application with increased user scale, we present a set of cost-aware adaptive control rules. The implementation of the rules is based on an analytical queuing model that determines the performance states of the IoT-based application, given the current workload and the allocated cloud/fog resources. Using the IoT-based application in an exemplar VRLE use case, we evaluate the cost-performance trade-offs with three system architectures i.e., cloud-only, edge-only and edge-cloud architectures. Experiment results illustrate the best/worst practices in the cost-performance trade-offs for a range of simulated IoT scenarios involving monitoring user emotional data collected by using brain sensors. Our results also detail the impact of the system architecture selection, and the benefits in enabling feedback about student emotions to instructors during Social VR learning sessions. Lastly, we show the benefits of integrating our model-based feedback control in maximizing IoT-based application performance while keeping the associated costs at a minimum level.
Short‐term photovoltaic power dynamic weighted combination forecasting based on least squares method

In recent years, in photovoltaic (PV) power forecasting research, there are certain limitations in single forecasting methods. In traditional combined forecasting methods, such as the average weight combined method and the fixed weight combined method, the determination of the weight value cannot guarantee that the forecasting error at each moment is the smallest. In order to reduce the PV power forecasting error, this paper proposes a short‐term PV power dynamic weighted combination forecasting based on the least squares (LS) method. First, the random components of the PV power are extracted using the periodogram method, and then the dynamic weight value of each method is determined with the LS method. The combined model forecasts the random components of PV power and superimposes them with the periodic component to obtain the final PV power forecast. Using the data from a PV power plant in Ashland, USA, we verify the effectiveness of the proposed method proposed. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Non-Markovianity in experimentally simulated quantum channels: Role of counterrotating-wave terms

We experimentally investigate the non-Markovianity problems in a quantum channel implemented by an all-optical setup. The quantum channel simulates the dynamics of a two-level (qubit) system surrounded by bosonic modes, with the system-environment interaction treated without applying the rotating-wave approximation (RWA). Therefore, it allows us to demonstrate the important role of counterrotating-wave terms in influencing the memory effects of the environment. The experimental and theoretical results are in good agreement and show quite different behaviors of the non-Markovianity measure between RWA and non-RWA channels. One can find the nonmonotonic dependence of the non-Markovianity measure on the coupling and detuning parameters in the non-RWA case, which is attributed to the competing mechanisms caused by the counterrotating-wave terms. We emphasize that the non-RWA channel provides a more clear description of the open-system dynamics and the counterrotating-wave terms are indispensable in the study of non-Markovianity.
An Analysis of the Short Utterance Problem for Speaker Characterization

Speaker characterization has always been conditioned by the length of the evaluated utterances. Despite performing well with large amounts of audio, significant degradations in performance are obtained when short utterances are considered. In this work we present an analysis of the short utterance problem providing an alternative point of view. From our perspective the performance in the evaluation of short utterances is highly influenced by the phonetic similarity between enrollment and test utterances. Both enrollment and test should contain similar phonemes to properly discriminate, being degraded otherwise. In this study we also interpret short utterances as incomplete long utterances where some acoustic units are either unbalanced or just missing. These missing units are responsible for the speaker representations to be unreliable. These unreliable representations are biased with respect to the reference counterparts, obtained from long utterances. These undesired shifts increase the intra-speaker variability, causing a significant loss of performance. According to our experiments, short utterances (3–60 s) can perform as accurate as if long utterances were involved by just reassuring the phonetic distributions. This analysis is determined by the current embedding extraction approach, based on the accumulation of local short-time information. Thus it is applicable to most of the state-of-the-art embeddings, including traditional i-vectors and Deep Neural Network (DNN) xvectors.