Reviewerly/exHarmony huggingface dataset:
Enclaves in the Clouds

With organizational data practices coming under increasing scrutiny, demand is growing for mechanisms that can assist organizations in meeting their data-management obligations. TEEs (trusted execution environments) provide hardware-based mechanisms with various security properties for assisting computation and data management. TEEs are concerned with the confidentiality and integrity of data, code, and the corresponding computation. Because the main security properties come from hardware, certain protections and guarantees can be offered even if the host privileged software stack is vulnerable.
Shing: A Conversational Agent to Alert Customers of Suspected Online-payment Fraud with Empathetical Communication Skills

Alerting customers on suspected online-payment fraud and persuade them to terminate transactions is increasingly requested with the rapid growth of digital finance worldwide. We explored the feasibility of using a conversational agent (CA) to fulfill this request. Shing, a voice-based CA, proactively initializes and repairs the conversation with empathetical communication skills in order to alert customers when a suspected online-payment fraud is detected, collects important information for fraud scrutiny and persuades customers to terminate the transaction once the fraud is confirmed. We evaluated our system by comparing it with a rule-based CA with regards to customer response and perceptions in a real-world context where our systems took 144,795 phone calls in total in which 83,019 (57.3%) natural breakdowns happened. Results showed that more customers stopped risky transactions after conversing with Shing. They seemed more willing to converse with Shing for more dialogue turns and provide transaction details. Our work presents practical implications for the design of proactive CA.
Comparing Sentiment Analysis of Indonesian Presidential Election 2019 with Support Vector Machine and K-Nearest Neighbor Algorithm

The purpose of this research is to compare the effectiveness of the Support Vector Machine (SVM) algorithm with the K-Nearest Neighbor (KNN) algorithm in predicting the results of the Indonesian presidential election 2019 based on sentiment analysis on social media (Twitter). The research methodology used includes several stages: preprocessing and weighting using TF-IDF. The SVM algorithm has the highest accuracy compared to the KNN algorithm. The average accuracy of SVM algorithm is 69.27, with the highest accuracy is 76.5%, while the average value of the KNN algorithm is 61.3% with the highest accuracy of 68.3%. The fastest training time is obtained by the KNN algorithm, while the SVM algorithm obtains the fastest testing time. The results of presidential predictions based on positive sentiment, namely candidate number 01 obtained a percentage of 67.98% while the number of positive sentiment predictions from candidate number 02 was 67.79%.
Hidden Markov Model Diarisation with Speaker Location Information

Speaker diarisation methods often rely on speaker embeddings to cluster together the segments of audio that are uttered by the same speaker. When the audio is captured using a microphone array, it is possible to estimate the locations of where the sounds originate from. This location information may be complementary to the speaker embeddings in the diarisation processes. This report proposes to extend the Hidden Markov Model (HMM) clustering method, to enable the use of speaker location information. The HMM observation log-likelihood for the speaker location can take the form of a KL-divergence, when the speaker location is represented as a discrete posterior distribution of the probabilities that the sound originated from each possible location. Experimental results on a Microsoft rich meeting transcription task show that using speaker location information with the proposed HMM modification can yield performance improvements over using speaker embeddings alone.
Information security governance metrics: a survey and taxonomy

Information Security Governance (ISG) is now considered a vital component of any organization's Information Technology (IT) Governance. ISG consists of the processes, organizational structures, and most importantly, the corporate leadership involved in the safeguarding of organization's information assets. Hence, the purpose of ISG is to bring information security to the attention of the executives such as CEOs and Boards, so that the executives can address the issues of information security and take security-related decisions that lead to outcomes that better align with organizational goals such as value delivery, better performance measurement, business process assurance, and risk management. In order for the corporate leadership to make data-driven decisions, data related to various security metrics are collected and presented in the form of dashboards. The goal of this article is to identify those security metrics that are particularly important from an ISG standpoint. A survey was performed on security literature to identify and categorize ISG metrics. An ISG metrics taxonomy was developed as a result of this study. Security teams can benefit from the ISG metrics taxonomy as, when creating security dashboards, the taxonomy can focus their attention on those specific security metrics that are of most value to the corporate leadership.
Meta-Learning for Cross-Channel Speaker Verification

Automatic speaker verification (ASV) has been successfully deployed for identity recognition. With increasing use of ASV technology in real-world applications, channel mismatch caused by the recording devices and environments severely degrade its performance, especially in the case of unseen channels. To this end, we propose a meta speaker embedding network (MSEN) via meta-learning to generate channel-invariant utterance embeddings. Specifically, we optimize the differences between the embeddings of a support set and a query set in order to learn a channel-invariant embedding space for utterances. Furthermore, we incorporate distribution optimization (DO) to stabilize the performance of MSEN. To quantitatively measure the effect of MSEN on unseen channels, we specially design the generalized cross-channel (GCC) evaluation. The experimental results on the HI-MIA corpus demonstrate that the proposed MSEN reduce considerably the impact of channel mismatch, while significantly outperforms other state-of-the-art methods.
Bayesian multivariate quantile regression using Dependent Dirichlet Process prior

In this article, we consider a non-parametric Bayesian approach to multivariate quantile regression. The proposed approach involves modeling of related conditional distributions of a response vector given the covariates using a Dependent Dirichlet Process (DDP) prior. The DDP is used to introduce dependence across covariates. The flexible covariate-dependent mixture of multivariate Gaussian kernels gives rise to an induced posterior for the desired multivariate quantile. For posterior computations, we use a truncated stick-breaking representation of the DDP, and use a block Gibbs sampler for estimating the model parameters. We illustrate our method with simulation studies, and a data containing blood pressures of 40 women. Finally, we provide a theoretical justification for the proposed method through posterior consistency and support properties of the prior.
HFP: Hardware-Aware Filter Pruning for Deep Convolutional Neural Networks Acceleration

Convolutional Neural Networks (CNNs) are powerful but computationally demanding and memory intensive, thus impeding their practical applications on resource-constrained hardware. Filter pruning is an efficient approach for deep CNN compression and acceleration, which aims to eliminate some filters with tolerable performance degradation. In the literature, the majority of approaches prune networks by defining the redundant filters or training the networks with a sparsity prior loss function. These approaches mainly use FLOPs as their speed metric. However, the inference latency of pruned networks cannot be directly controlled on the hardware platform, which is an important dimension of practicality. To address this issue, we propose a novel Hardware-aware Filter Pruning method (HFP) which can produce pruned networks that satisfy the actual latency budget on the hardwares of interest. In addition, we propose an iterative pruning framework called Opti-Trim to decrease the accuracy degradation of pruning process and accelerate the pruning procedure whilst meeting the hardware budget. More specifically, HFP first builds up a lookup table for fast estimating the latency of target network about filter configuration layer by layer. Then, HFP leverages information gain (IG) to globally evaluate the filters contribution to network output distribution. HFP utilizes the Opti-Trim framework to globally prune filters with the minimum IG one by one until the latency budget is satisfied. We verify the effectiveness of the proposed method on CIFAR-10 and ImageNet. Compared with the state-of-the-art pruning methods, HFP demonstrates superior performances on VGGNet, ResNet and MobileNet V1/V2.
Attention Pyramid Module for Scene Recognition

The unrestricted open vocabulary and diverse substances of scenery images bring significant challenges to scene recognition. However, most deep learning architectures and attention methods are developed on general-purpose datasets and omit the characteristics of scene data. In this paper, we exploit the Attention Pyramid Module (APM) to tackle the predicament of scene recognition. Our method streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the interdependency among scales, and further assists feature re-calibration as well as the aggregation process. APM is extremely light-weighted and can be plugged into existing network architectures in a parameter-efficient manner. By integrating APM into ResNet-50, we obtain a boost of top-1 accuracy by 3.54% on the benchmark dataset. Our comprehensive experiments demonstrate that APM achieves much improved performance comparing with the state-of-the-art attention methods using significantly less computation budget. Source code is provided on https://github.com/ZN-Qiao/APM.
Robotics: Biological Hypercomputation and Bio-Inspired Swarms Intelligence

The concept of hyper computing (BH) has been introduced to understand how living systems process information. This article presents the BH developments but supports the idea that living beings communicate in structures and not in the mode of signs and symbols. Genetic algorithms are bio-inspired optimization algorithms that simulate the process of the natural evolution of species. They make it possible to manipulate a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of genetic algorithms for statistical machine translation. The bio-inspired communitarian literature proposes a communication model to capture the nature of individuals' interaction. The research-based on metrics, topology, and algorithms are derived from the bio-inspired communication model's visual investigation. The evaluation's assumption is the choice of biologically inspired communication models can influence group performance for a specific task. The communication model was evaluated in two environments. Swarm mission: search for targets communicated among others and avoid opponents. Overall results of the survey Prove that the group agent has the best overall performance when used a bio-inspired communication model to look for specific tasks and avoid compliance with best practices than hostile tasks when using topology models. This study's main cause is to magnify the group's mission's performance by deliberately selecting the bio-inspired communication model.
mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast

Time-series forecasting contributes crucial information to industrial and institutional decision-making with multivariate time-series input. Although various models have been developed to facilitate the forecasting process, they make inconsistent forecasts. Thus, it is critical to select the model appropriately. The existing selection methods based on the error measures fail to reveal deep insights into the model’s performance, such as the identification of salient features and the impact of temporal factors (e.g., periods). This paper introduces mTSeer, an interactive system for the exploration, explanation, and evaluation of multivariate time-series forecasting models. Our system integrates a set of algorithms to steer the process, and rich interactions and visualization designs to help interpret the differences between models in both model and instance level. We demonstrate the effectiveness of mTSeer through three case studies with two domain experts on real-world data, qualitative interviews with the two experts, and quantitative evaluation of the three case studies.
Co-Attentional Transformers for Story-Based Video Understanding

Inspired by recent trends in vision and language learning, we explore applications of attention mechanisms for visio-lingual fusion within an application to story-based video understanding. Like other video-based QA tasks, video story understanding requires agents to grasp complex temporal dependencies. However, as it focuses on the narrative aspect of video it also requires understanding of the interactions between different characters, as well as their actions and their motivations. We propose a novel co-attentional transformer model to better capture long-term dependencies seen in visual stories such as dramas and measure its performance on the video question answering task. We evaluate our approach on the recently introduced DramaQA dataset which features character-centered video story understanding questions. Our model outperforms the baseline model by 8 percentage points overall, at least 4.95 and up to 12.8 percentage points on all difficulty levels and manages to beat the winner of the DramaQA challenge.
Research and Implementation of Chinese Couplet Generation System With Attention-Based Transformer Mechanism

Couplet is a unique art form in Chinese traditional culture. The development of deep neural network (DNN) technology makes it possible for computers to automatically generate couplets. Especially, Transformer is a DNN-based “Encoder–Decoder” framework, and widely used in natural language processing (NLP). However, the existed Transformer mechanism cannot fully exploit the essential linguistic knowledge in Chinese, including the special format and requirements of Chinese couplets. Therefore, this article adapts the Transformer mechanism to generate meaningful Chinese couplets. Specifically, the contributions of our work are threefold. First, considering the fact that the words in the corresponding positions of the antecedent clause and the subsequent clause in a Chinese couplet always have same part-of-speech (pos, i.e., word class), pos information is intentionally added into the Transformer to improve the accuracy of the conceived couplet. Second, to deal with the large number of unregistered and low-frequency words in Chinese couplet, a specific unregistered/low-frequency word processing mechanism (UWP) is designed and combined with the Transformer model. Third, to further improve the coherence of couplets, we incorporate the polish mechanisms (PMs) into Transformer model. In terms of three evaluation criteria including bilingual evaluation understudy (BLEU), perplexity, and human evaluation, the experimental results demonstrate the effectiveness of our designed Chinese couplet generation system.
Interface to Query and Visualise Definitions from a Knowledge Base

Linked data is at the core of the Web due to its ability to model real world entities, connect them via relationships and provide context, which could help to transform data into information and information into knowledge. For example, ontologies, which could be stored locally or could be made available to everyone online (e.g. the DBpedia knowledge base). However, both access and usage of Linked Data require individuals to have knowledge in the field of the Semantic Web. Many of the existing solutions are developed for specific use cases such as building and exploring ontologies visually and are aimed at expert users. The solutions that are aimed at non-experts are generic and, in most cases, a data visualisation is not available. In this paper, we present a web application with a user interface (UI), which combines features from applications for both expert and non-experts. The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base for definitions of a specific word and to view a graphical visualisation of the query results (the search keyword itself and concepts related to it).
Post-Post-it: A Spatial Ideation System in VR for Overcoming Limitations of Physical Post-it Notes

Post-it notes are great problem-solving tools. However, physical Post-it notes have limitations: surfaces for attaching them can run out; rearranging them can be labor-intensive; documenting and storing them can be cumbersome. We present Post-Post-it, a novel VR interaction system that overcomes these physical limitations. We derived design requirements from a formative study involving a problem-solving meeting using Post-it notes. Then, through physical prototyping, using physical materials such as Post-it notes, transparent acrylic panels, and masking tape, we designed a set of lifelike VR interactions based on hand gestures that the user can perform easily and intuitively. With our system, the user can create and place Post-it notes in an immersive space that is large enough to ideate freely, quickly move, copy, or delete many Post-it notes at once, and easily manage the results.
Language as the tip of the iceberg? Shedding a critical light on ‘hidden’ discourse in digital platforms

In digital scenarios, procedural instructions are instances of a kind of discourse that is pervasive yet little visible. These instructions define the information architectures of digital platforms themselves (van Dijck et al. 2019), construct the symmetries of relationships by determining hierarchies and dependencies among users and shape the context of digital communication. The pervasiveness of technical documentation, however, is inversely proportional to its accessibility, as this is generally hidden to the public behind the level of user interfaces. However, the impact that these instructions have on a wide range of discourse practices for end users (for example in terms of how apps are developed or how users’ data are protected), make them worthy of investigation. By building on critical multimodal discourse analysis (van Leeuwen, 2012, van Leeuwen, 2014, Machin, 2013) and integrating discourse analysis with elements of information theory (Moschini 2018), we will analyze a set of Facebook technical documentation about the login authentication service, comparing how this service was presented to app developers before and after the Cambridge Analytica scandal (Cadwalladr and Graham-Harrison 2018). We will show that, far from being a neutral authentication service, Facebook Login indexes discourse practices that enact specific social roles. We will do so to shed a critical light on the multidimensional micro-circulation of power (Foucault 1980) by means of texts analysis of instructional tools, by triangulating heuristics from linguistics, multimodality and information theory.
Inpainting-Based Video Compression in FullHD

Compression methods based on inpainting are an evolving alternative to classical transform-based codecs for still images. Attempts to apply these ideas to video compression are rare, since reaching real-time performance is very challenging. Therefore, current approaches focus on simplified frame-by-frame reconstructions that ignore temporal redundancies. As a remedy, we propose a highly efficient, real-time capable prediction and correction approach that fully relies on partial differential equations (PDEs) in all steps of the codec: Dense variational optic flow fields yield accurate motion-compensated predictions, while homogeneous diffusion inpainting is applied for intra prediction. To compress residuals, we introduce a new highly efficient block-based variant of pseudodifferential inpainting. Our novel architecture outperforms other inpainting-based video codecs in terms of both quality and speed. For the first time in inpainting-based video compression, we can decompress FullHD (1080p) videos in real-time with a fully CPU-based implementation, outperforming previous approaches by roughly one order of magnitude.
CARE: Lightweight Attack Resilient Secure Boot Architecture with Onboard Recovery for RISC-V based SOC

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the software to a benign state. This manuscript presents CARE - the first secure boot framework that provides malicious code modification attack detection, resilience, and onboard recovery mechanism for the compromised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the integrity and authenticity of the software and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to provide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides promising resilience and recovery mechanism with very little (8%) performance and resource overhead.
Construction Technology of Knowledge Graph and its Application in Power Grid

With the rapid development of energy internet, dispatchers need to learn more knowledge with wider scope and fast update speed. It’s urgent to realize the knowledge electrification, knowledge systematization, knowledge visualization and knowledge sharing of power grid dispatching. Firstly, this paper studies the definition and traits of knowledge graph, deeply analyses the key technologies of knowledge graph construction. Secondly, considering the contents and requirements of power grid dispatching business as well as the internal relationship of basic theoretical knowledge of power grid operation analysis, a method of constructing AC/DC power grid dispatching knowledge property graph is proposed. Finally, a practical example of a regional power grid knowledge graph is built to verify the effectiveness and practicality of the proposed method.
DIRECTDEBUG: Automated Testing and Debugging of Feature Models

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DirectDebug which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.