Reviewerly/exHarmony huggingface dataset:
Subgroup Invariant Perturbation for Unbiased Pre-Trained Model Prediction

Modern deep learning systems have achieved unparalleled success and several applications have significantly benefited due to these technological advancements. However, these systems have also shown vulnerabilities with strong implications on the fairness and trustability of such systems. Among these vulnerabilities, bias has been an Achilles' heel problem. Many applications such as face recognition and language translation have shown high levels of bias in the systems towards particular demographic sub-groups. Unbalanced representation of these sub-groups in the training data is one of the primary reasons of biased behavior. To address this important challenge, we propose a two-fold contribution: a bias estimation metric termed as Precise Subgroup Equivalence to jointly measure the bias in model prediction and the overall model performance. Secondly, we propose a novel bias mitigation algorithm which is inspired from adversarial perturbation and uses the PSE metric. The mitigation algorithm learns a single uniform perturbation termed as Subgroup Invariant Perturbation which is added to the input dataset to generate a transformed dataset. The transformed dataset, when given as input to the pre-trained model reduces the bias in model prediction. Multiple experiments performed on four publicly available face datasets showcase the effectiveness of the proposed algorithm for race and gender prediction.
Digital forensic readiness intelligence crime repository

Abstract It may not always be possible to conduct a digital (forensic) investigation post‐event if there is no process in place to preserve potential digital evidence. This study posits the importance of digital forensic readiness, or forensic‐by‐design, and presents an approach that can be used to construct a Digital Forensic Readiness Intelligence Repository (DFRIR). Based on the concept of knowledge sharing, the authors leverage this premise to suggest an intelligence repository. Such a repository can be used to cross‐reference potential digital evidence (PDE) sources that may help digital investigators during the process. This approach employs a technique of capturing PDE from different sources and creating a DFR repository that can be able to be shared across diverse jurisdictions among digital forensic experts and law enforcement agencies (LEAs), in the form of intelligence. To validate the approach, the study has employed a qualitative approach based on a number of metrics and an analysis of experts' opinion has been incorporated. The DFRIR seeks to maximize the collection of PDE, and reducing the time needed to conduct forensic investigation (e.g., by reducing the time for learning). This study then explains how such an approach can be employed in conjunction with ISO/IEC 27043: 2015.
TADO: Time-Varying Attention with Dual-Optimizer Model

The review-based recommender systems are commonly utilized to measure users' preferences towards different items. In this paper, we focus on addressing three main problems existing in the review-based methods. Firstly, these methods suffer from the class-imbalanced problem where rating levels with lower proportions will be ignored to some extent. Thus, their performance on relatively rare rating levels is unsatisfactory. As the first attempt in this field to address this problem, we propose a flexible dual-optimizer model to gain robustness from both regression loss and classification loss. Secondly, to address the problem caused by the insufficient contextual information extraction ability of word embedding, we first introduce BERT into the review-based method to improve the performance of the semantic analysis. Thirdly, the existing methods ignore the feature information of the time-varying user preferences. Therefore, we propose a time-varying feature extraction module with bidirectional long short-term memory and multi-scale convolutional neural network. Afterward, an interaction component is proposed to further summarize the contextual information of the user-item pairs. To verify the effectiveness of the proposed TADO, we conduct extensive experiments on 23 benchmark datasets selected from Amazon Product Reviews. Compared with several recently proposed state-of-the-art methods, our model obtains significantly gain over ALFM, MPCN, and ANR averagely with 20.98%, 9.84%, and 15.46%, respectively. Further analysis proves the necessity of jointly using the proposed components in TADO.
Modelling spatio-temporal ageing phenomena with deep Generative Adversarial Networks

Deterioration modelling of ageing phenomena on materials is an actively researched topic in computer graphics and vision, with a wide range of applications in domains such as cultural heritage, game programming, material science and virtual reality. As a result significant progress has been accomplished and existing methods are able to produce visually pleasing results that appear realistic. However, there is a very limited connection to comprehensive measurements that actually capture the ageing process of a material. This paper focuses on this gap, aiming to provide a link between physical measurements and deterioration modelling. Based on extensive measurements of texture and surface geometry of artificially aged reference materials, a Deep Learning (DL) framework is proposed that models spatio-temporal variations on the 3D surface geometry and the 2D colour–image appearance. Concretely, the problem of material degradation over time is formulated as an 2D/3D material-to-material translation problem, where the goal is, given an input material and a target degradation time, to output the degraded material at that time. At the core of the method lies a modified conditional Generative Adversarial Network (cGAN), which maps input materials to degraded materials over time. In order to train and deploy the proposed cGAN model, proper data parameterization and augmentation steps are introduced. As shown through extensive experimentation on real data coming from materials commonly found in artwork and from actual artworks, the proposed approach produces high quality results.
An Accumulator-Based Revocation in Delegatable Anonymous Credentials

For privacy-enhancing user authentication, anonymous credential system was proposed. In the system, a user is issued a credential on attributes from an issuer, and the user can anonymously prove the ownership of the credential. As the extension, a delegatable anonymous credential (DAC) system was proposed. In the DAC system, the owner of a credential can hierarchically delegate it to another entity, who can also issue a credential to lower entities. Since intermediate issuers in the chaining credentials can be hidden, the DAC system is considered to be applied to a permissioned blockchain. Furthermore, to enable the revocation of credentials, a revocable DAC system was proposed. However, in the previously proposed revocable DAC system, an issuer, who manages the user group, has to issue the non-revocation credentials to all non-revoked users at every epoch, and thus the issuer can be in a bottleneck and the communication cost is high. In this paper, we propose a revocable DAC system using an accumulator. In the proposed system, only a single accumulator and the credential on the accumulator are published at every epoch. Thus there is no bottleneck of the issuer and the communication cost is very low.
Application of Digital Signal Processors based on Methods of Spline-Functions for One-Dimensional Signals Processing

This paper is devoted to the issues of using digital signal processors for processing and smoothing one-dimensional signals and experimental data. The use of cubic basic splines and smoothing splines for approximating signals is proposed. VisualDSP++ simulator was used as a medium for modelling of signal processing processes, as well as development and debugging of programs for digital signal processors. This environment includes an easy-to-use assembler, a C/C++ compiler, and a C/C++ executable function library that includes mathematical and DSP functions. A key feature of the simulator is the effectiveness of code written in C/C++. The results of processing and smoothing of one-dimensional seismic signals and experimental data obtained as a result of magnetic prospecting are also presented.
Longevity of risks in software development projects: a comparative analysis with an academic environment

The study presented in this paper was developed in an academic environment. All subjects involved in this research are enrolled in the Information Systems Master's Degree of the University of Minho. The main objective of this study is to demonstrate the need for the continuity of studies about the risks presented in software development projects. For this purpose, we analyzed more than four hundred risks and about one hundred problems faced and documented by the working groups. With the collected data we defined a list of twenty risks and conducted a comparative study of these risks with others already formalized in previous industrial studies. The comparison data was analyzed and interpreted, and important conclusions were made.
Harnessing Tensor Structures—Multi-Mode Reservoir Computing and Its Application in Massive MIMO

In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC). It inherits the dynamic mechanism of RC and processes the forward path and loss optimization of the NN using tensor as the underlying data format. Multi-Mode RC exhibits less complexity compared to conventional RC structures (e.g. single-mode RC), and offers comparable generalization performance to its single-mode counterpart. Furthermore, we introduce an alternating least square-based learning algorithm as well as the associated theoretical analysis for Multi-Mode RC. The result can be utilized to guide the configuration of NN parameters to sufficiently circumvent over-fitting issues. As a key application, we consider the symbol detection task in multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems with massive MIMO employed at the base stations (BSs). Thanks to the tensor structure of massive MIMO-OFDM signals, our online learning-based symbol detection method generalizes well in terms of bit error rate even using a limited online training set. Evaluation results suggest that the Multi-Mode RC-based learning framework can efficiently and effectively combat practical constraints of wireless systems (i.e. channel state information (CSI) errors and hardware non-linearity) to enable robust and adaptive communications over the air.
Learning to Respond with Your Favorite Stickers

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching the stickers image with previous utterances. However, existing methods usually focus on measuring the matching degree between the dialog context and sticker image, which ignores the user preference of using stickers. Hence, in this article, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user. Two main challenges are confronted in this task. One is to model the sticker preference of user based on the previous sticker selection history. Another challenge is to jointly fuse the user preference and the matching between dialog context and candidate sticker into final prediction making. To tackle these challenges, we propose a Preference Enhanced Sticker Response Selector (PESRS) model. Specifically, PESRS first employs a convolutional-based sticker image encoder and a self-attention-based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker and each utterance. Then, we model the user preference by using the recently selected stickers as input and use a key-value memory network to store the preference representation. PESRS then learns the short-term and long-term dependency between all interaction results by a fusion network and dynamically fuses the user preference representation into the final sticker selection prediction. Extensive experiments conducted on a large-scale real-world dialog dataset show that our model achieves the state-of-the-art performance for all commonly used metrics. Experiments also verify the effectiveness of each component of PESRS.
Markov Models for Multi-state Language Change

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Low Power Reversible Parallel and Serial Binary Adder/Subtractor

Each and every day new technology is being established which is miniaturized and challenging the low power than existing design. All the gates used in the design is reversible to achieve low power and less complexity. In past few decades reversible enlarges its applications in modern computing environment. Reversible gates can be executed using any Boolean function. In a modern computing environment adders plays important role for addition process as well as for subtraction. This paper proposes a modified reversible parallel and serial adder/subtraction circuit using Dual Key Gate (DKG) and SG gate. The performance of parallel adder/subtractor circuit design using dual key gate and serial adder/subtractor using dual key gate with SG gate is simulated and synthesized using Xilinx. The performance of this circuit is compared with existing design using Feynman gate and toffoli gate based on complexity, low power and Garbage input/outputs.
Extra Large Aperture FPCB Mirror Based Scanning LiDAR

This article presents an extra-large (25 × 50 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) flexible printed circuit board (FPCB) mirror to cover both the emitting and receiving lenses of a single-point LiDAR to form a biaxial scanning LiDAR with a compact structure and long measurement distance and low cost. The FPCB mirror is fabricated using the low cost and commercially available FPCB fabrication process. In addition, a widely available single–point LiDAR is used as the measurement unit. This article's novelty lies in the following two points: 1) integrating a large aperture scanning mirror and a single-point LiDAR (low cost and widely available) to construct a biaxial scanning LiDAR; 2) proposed a large aperture FPCB mirror using long existing FPCB process such as to achieve low cost. The scanning LiDAR is designed for applications in factory for robots and automated guided vehicles; navigation. The FPCB mirror consists of a mirror plate, two permanent magnets, and a FPCB structure, which includes two torsion beams, a middle seat, and a Flame retardant 4 (FR4) stiffener frame. Four-layer copper coils are embedded in the FPCB structure, which is fabricated using the low cost commercially available FPCB fabrication process. The mirror plate is diced from a thin silicon mirror plate with gold coating and then attached on top of the middle seat of the FPCB structure. With such large aperture FPCB scanning mirror, and a long measurement distance single-point LiDAR, a compact biaxial scanning LiDAR is constructed. The scanning LiDAR based on the FPCB mirror is constructed and tested. Achieved performances are: the field of view of 60°, measurement distance of 50 m, refresh rate of 20 Hz, 500 points for each scanning frame.
A Brain-Inspired Self-Organizing Episodic Memory Model for a Memory Assistance Robot

This article discusses the implementation of a brain-inspired episodic memory model, which provides memory assistance and tackles the modern public issue of memory impairment embedded as an end-to-end system on the robot companion, Pepper. Based on the fusion adaptive resonance theory, the proposed model can observe and memorize the content of daily events in five aspects: 1) people; 2) activities; 3) times; 4) places; and 5) objects. The model is based on the human memory pipeline, containing a working memory and a two-layer long-term memory model, which can effectively merge, cluster, and summarize past memories based on their context and relevance in a self-organizing manner. When providing memory assistance, the robot can analyze a user query and find the best matching memory cluster to generate verbal cues to stimulate recalling of the target event. Moreover, using reinforcement learning, the robot eventually learns the most effective mapping of cue types to event type through social interaction. Experiments show the feasibility of the proposed model, which can handle episodic events with elasticity and stability. Moreover, there is evidence that the robot is able to provide robust memory assistance from knowledge obtained through previous observations, with 99% confidence, intervals in the participants’ mean recall percentage of the events increases 19.63% after receiving memory assistance from the robot.
Autonomous Navigation over Europa Analogue Terrain for an Actively Articulated Wheel-on-Limb Rover

The ocean world Europa is a prime target for exploration given its potential habitability [1]. We propose a mobile robotic system that is capable of autonomously traversing tens of meters to visit multiple sites of interest on a Europan analogue surface. Due to the topology of Europan terrain being largely unknown, it is desired that this mobility system traverse a large variety of terrain types. The mobility system should also be capable of crossing unstructured terrain in an autonomous manner given the communications limitations between Earth and Europa.A wheel-on-limb robotic rover is presented that may actively conform to terrain features up to 1.5 wheel diameters tall while driving. The robot uses a sampling-based motion planner to generate paths that leverage its unique locomotive capabilities. The planner assesses terrain hazards and wheel workspace limits as obstacles. It may also select a mobility mode based on predicted energy usage and the need for limb articulation on the terrain being traversed. This autonomous mobility was evaluated on chaotic salt-evaporite terrain found in Death Valley, CA, an analogue to the Europan surface. Over the course of 38 trials, the rover autonomously traversed 435m of extreme terrain while maintaining a rate of 0.64 traverse ending failures for every 10m driven.
Young students’ treatment of synthetic voicing as an interactional resource in digital writing

This multimodal conversation analysis study is part of a larger video ethnographic project that explores the media literacy practices that children develop as they use digital and mobile technologies.The study investigates how Swedish students in grades 3-4 make use of text to speech (TTS) technology as an interactional resource during collaborative writing on iPads in the classroom.The results show that students routinely make use of synthetic voicings to display and claim knowledge about the voiced written units and negotiate writing roles with differing epistemic rights and obligations to assess voicings and practice repair.
Pre-Service Teachers’ Immersive Experience in Virtual Classroom

In this paper, the results of two pilot studies, carried out in a 3D classroom simulator (based on the Unity 3D engine), will be presented. A virtual classroom user uses a virtual reality headset, and teaches virtual learners (didactic-controlled), who report, ask, and answer questions, but also disturb. A total of 10 future geography teachers and 5 future computer science teachers participated in the research. In the virtual classroom, they underwent three-phase training, which was aimed at developing their pedagogical-psychological skills. In the first phase, they briefly tried working in the virtual classroom, so that they could prepare more accurately for the second phase, during which they were asked to explain the selected curriculum, or to assign virtual or individual work to virtual pupils. The performance in the virtual classroom was always followed by an in-depth analysis of the output with the facilitator and other students. A video recording of the whole performance was also made for students’ self-reflection. Students had the opportunity to reflect on their output from various points of view, and prepare for the last phase—repetition of the output in the virtual classroom (this time with the inclusion of comments, new approaches to the output, etc.). The research showed that reflective lessons in the virtual classroom have enormous potential for opening specific didactic topics during didactics lessons, both for the development of the study group discussion, and for deep reflection. In the third phase of the training, all of the students observed an improvement in a wide range of aspects of teaching. In the third phase of training, most students also experienced strong immersion—students get used to the virtual classroom, almost to the degree of forgetting where they are in the real world. This is all in spite the fact that the current prototype of the virtual classroom used is only at the beginning of its development, and will be gradually modified, complemented, and developed.
User-Managed Access Delegation for Blockchain-driven IoT Services

With the advancements in network bandwidth and hardware capability, the Internet of Things (IoT) has now become more and more prevalent. Nevertheless, due to the increment of the data value, incidents of malicious attacks are also spreading. Therefore, the robust access control mechanism plays an important role when designing an IoT system. Regrettably, most of the IoT devices have the limitation of the power and computation capacity. To tackle this issue, it is general for the IoT system to delegate the access control service to the third party. However, centralized access control services may bring about some significant disadvantages such as lack of transparency and reliability. Moreover, it is difficult for users to track authorization history and manage authorization policies. Fortunately, blockchain is regarded as a promising technology that can cover the shortage of the centralized system and improve the security of IoT. This paper proposes a Blockchain-assisted User-Managed Access (B-UMA) schema base on the IoT scenario. To prove the feasibility of the proposed schema, we also build a prototype system adopting the "Smart Factory" use case. Finally, the qualitative comparison among B-UMA and related access control frameworks are also presented.
Enhancing the performance of social spider optimization with neighbourhood attraction algorithm

Abstract Data clustering is a well-known problem in order to identify the inherent structures and extracting the useful information. Recently, social spider optimization (SSO) algorithm is applied to solve a clustering problem. But, it may fall into premature convergence due to find improper nearest spiders in order to achieve the global solution. In this research paper, the neighbourhood attraction (NA) method is used to enhance the performance of the SSO clustering algorithm. In the experimental results, the proposed NA+SSO clustering method is producing better performance when compared with other conventional clustering algorithm.
Proposal and Evaluation of a Tabu List Based DQN for AAV Mobility

The Deep Q Network (DQN) is one of the methods of the deep reinforcement learning algorithm, which is a deep neural network structure used to estimate Q-values in Q-learning methods. The authors have previously designed and implemented a DQN-based mobility control methods for Autonomous Aerial Vehicle (AAV). In this paper, we propose and evaluate a DQN based on tabu list strategy for AAV mobility control. For evaluation, we simulate were conducted for the mobility control of AAV in a staircase environment using normal DQN and tabu list based DQN. The simulation results showed that a tabu list based DQN was a better solution than the normal DQN.