Reviewerly/exHarmony huggingface dataset:
Energy-Aware Task Allocation for Multi-Cloud Networks

In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks (ETAMCN)) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLA-violation of ETAMCN for different scenarios is performed.
Decentralized learning works: An empirical comparison of gossip learning and federated learning

Machine learning over distributed data stored by many clients has important applications in use cases where data privacy is a key concern or central data storage is not an option. Recently, federated learning was proposed to solve this problem. The assumption is that the data itself is not collected centrally. In a master–worker architecture, the workers perform machine learning over their own data and the master merely aggregates the resulting models without seeing any raw data, not unlike the parameter server approach. Gossip learning is a decentralized alternative to federated learning that does not require an aggregation server or indeed any central component. The natural hypothesis is that gossip learning is strictly less efficient than federated learning due to relying on a more basic infrastructure: only message passing and no cloud resources. In this empirical study, we examine this hypothesis and we present a systematic comparison of the two approaches. The experimental scenarios include a real churn trace collected over mobile phones, continuous and bursty communication patterns, different network sizes and different distributions of the training data over the devices. We also evaluate a number of additional techniques including a compression technique based on sampling, and token account based flow control for gossip learning. We examine the aggregated cost of machine learning in both approaches. Surprisingly, the best gossip variants perform comparably to the best federated learning variants overall, so they offer a fully decentralized alternative to federated learning.
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification

Visible-Infrared person re-identification (VI-ReID) aims to match cross-modality pedestrian images, breaking through the limitation of single-modality person ReID in dark environment. In order to mitigate the impact of large modality discrepancy, existing works manually design various two-stream architectures to separately learn modality-specific and modality-sharable representations. Such a manual design routine, however, highly depends on massive experiments and empirical practice, which is time consuming and labor intensive. In this paper, we systematically study the manually designed architectures, and identify that appropriately separating Batch Normalization (BN) layers is the key to bring a great boost towards cross-modality matching. Based on this observation, the essential objective is to find the optimal separation scheme for each BN layer. To this end, we propose a novel method, named Cross-Modality Neural Architecture Search (CM-NAS). It consists of a BN-oriented search space in which the standard optimization can be fulfilled subject to the cross-modality task. Equipped with the searched architecture, our method outperforms state-of-the-art counter-parts in both two benchmarks, improving the Rank-1/mAP by 6.70%/6.13% on SYSU-MM01 and by 12.17%/11.23% on RegDB. Code is released at https://github.com/JDAI-CV/CM-NAS.
Trust-Based Blockchain Authorization for IoT

Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as user’s attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts.
CFM: Convolutional Factorization Machines for Context-Aware Recommendation

Factorization Machine (FM) is an effective solution for context-aware recommender systems (CARS) which models second-order feature interactions by inner product. However, it is insufficient to capture high-order and nonlinear interaction signals. While several recent efforts have enhanced FM with neural networks, they assume the embedding dimensions are independent from each other and model high-order interactions in a rather implicit manner. In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations. Specifically, CFM models second-order interactions with outer product, resulting in ''images'' which capture correlations between embedding dimensions. Then all generated ''images'' are stacked, forming an interaction cube. 3D convolution is applied above it to learn high-order interaction signals in an explicit approach. Besides, we also leverage a self-attention mechanism to perform the pooling of features to reduce time complexity. We conduct extensive experiments on three real-world datasets, demonstrating significant improvement of CFM over competing methods for context-aware top-k recommendation.
Sentic Activation: A Two-Level Affective Common Sense Reasoning Framework

An important difference between traditional AI systems and human intelligence is our ability to harness common sense knowledge gleaned from a lifetime of learning and experiences to inform our decision making and behavior. This allows humans to adapt easily to novel situations where AI fails catastrophically for lack of situation-specific rules and generalization capabilities. Common sense knowledge also provides the background knowledge for humans to successfully operate in social situations where such knowledge is typically assumed. In order for machines to exploit common sense knowledge in reasoning as humans do, moreover, we need to endow them with human-like reasoning strategies. In this work, we propose a two-level affective reasoning framework that concurrently employs multi-dimensionality reduction and graph mining techniques to mimic the integration of conscious and unconscious reasoning, and exploit it for sentiment analysis.
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion in the Wild

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9,876 video clips of body movements and 13,239 human characters, named BoLD (Body Language Dataset), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton, and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans.
Software product line evolution: A systematic literature review

Context: Software Product Lines (SPL) evolve when there are changes in the requirements, product structure or the technology being used. Different approaches have been proposed for managing SPL assets and some also address how evolution affects these assets. Existing mapping studies have focused on specific aspects of SPL evolution, but there is no cohesive body of work that gives an overview of the area as a whole. Objective: The goals of this work are to review the characteristics of the approaches reported as supporting SPL evolution, and to synthesize the evidence provided by primary studies about the nature of their processes, as well as how they are reported and validated. Method: We conducted a systematic literature review, considering six research questions formulated to evaluate evolution approaches for SPL. We considered journal, conference and workshop papers published up until March 2017 in leading digital libraries for computer science. Results: After a thorough analysis of the papers retrieved from the digital libraries, we ended up with a set of 60 primary studies. Feature models are widely used to represent SPLs, so feature evolution is frequently addressed. Other assets are less frequently addressed. The area has matured over time: papers presenting more rigorous work are becoming more common. The processes used to support SPL evolution are systematic, but with a low level of automation. Conclusions: Our research shows that there is no consensus about SPL formalization, what assets can evolve, nor how and when these evolve. Case studies are quite popular, but few industrial-sized case studies are publicly available. Also, few of the proposed techniques offer tool support. We believe that the SPL community needs to work together to improve the state of the art, creating methods and tools that support SPL evolution in a more comparable manner.
SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks

Automatic generation of Chinese fonts that consist of large numbers of glyphs with complicated structures is now still a challenging and ongoing problem in areas of AI and Computer Graphics (CG). Traditional CG-based methods typically rely heavily on manual interventions, while recentlypopularized deep learning-based end-to-end approaches often obtain synthesis results with incorrect structures and/or serious artifacts. To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks. The key idea is to integrate the domain knowledge of Chinese characters with deep generative networks to ensure that high-quality glyphs with correct structures can be synthesized. More specifically, we first apply a CNN model to learn how to transfer the writing trajectories with separated strokes in the reference font style into those in the target style. Then, we train another CNN model learning how to recover shape details on the contour for synthesized writing trajectories. Experimental results validate the superiority of the proposed SCFont compared to the state of the art in both visual and quantitative assessments.
A novel chaotic image encryption algorithm based on content-sensitive dynamic function switching scheme

This paper proposes a novel chaotic image encryption algorithm based on a content-sensitive dynamic function switching mechanism. The proposed encryption algorithm is a symmetric cipher, operating on 1D byte sequence. Three independent chaotic maps are utilized for scrambling plain image bytes in order to realize confusion and diffusion properties. The first chaotic map is used for choosing one out of previously encrypted bytes, the second for choosing one out of remaining bytes not encrypted yet, and the last for determining a stochastic value to be externally injected into the stream simply by exclusive-or operation. An innovative content-sensitive dynamic function switching mechanism introducing more random behavior is incorporated in order to ensure high sensitivity to slight changes in plain image bytes. The proposed cryptosystem has gone through various common security analyses and tests to prove its validity. The results demonstrate that the proposed method achieves high security level regarding information entropy, key space, key sensitivity, and resistance to differential, data loss and noise attacks.
MyoSign

Automatic sign language recognition is an important milestone in facilitating the communication between the deaf community and hearing people. Existing approaches are either intrusive or susceptible to ambient environments and user diversity. Moreover, most of them perform only isolated word recognition, not sentence-level sequence translation. In this paper, we present MyoSign, a deep learning based system that enables end-to-end American Sign Language (ASL) recognition at both word and sentence levels. We leverage a lightweight wearable device which can provide inertial and electromyography signals to non-intrusively capture signs. First, we propose a multimodal Convolutional Neural Network (CNN) to abstract representations from inputs of different sensory modalities. Then, a bidirectional Long Short Term Memory (LSTM) is exploited to model temporal dependences. On the top of the networks, we employ Connectionist Temporal Classification (CTC) to get around temporal segments and achieve end-to-end continuous sign language recognition. We evaluate MyoSign on 70 commonly used ASL words and 100 ASL sentences from 15 volunteers. Our system achieves an average accuracy of 93.7% at word-level and 93.1% at sentence-level in user-independent settings. In addition, MyoSign can recognize sentences unseen in the training set with 92.4% accuracy. The encouraging results indicate that MyoSign can be a meaningful buildup in the advancement of sign language recognition.
Cyber Security Threat Modeling for Supply Chain Organizational Environments

Cyber security in a supply chain (SC) provides an organization the secure network facilities to meet its overall business objectives. The integration of technologies has improved business processes, increased production speed, and reduced distribution costs. However, the increased interdependencies among various supply chain stakeholders have brought many challenges including lack of third party audit mechanisms and cascading cyber threats. This has led to attacks such as the manipulation of the design specifications, alterations, and manipulation during distribution. The aim of this paper is to investigate and understand supply chain threats. In particular, the paper contributes towards modeling and analyzing CSC attacks and cyber threat reporting among supply chain stakeholders. We consider concepts such as goal, actor, attack, TTP, and threat actor relevant to the supply chain, threat model, and requirements domain, and modeled the attack using the widely known STIX threat model. The proposed model was analyzed using a running example of a smart grid case study and an algorithm to model the attack. A discrete probability method for calculating the conditional probabilities was used to determine the attack propagation and cascading effects, and the results showed that our approach effectively analyzed the threats. We have recommended a list of CSC controls to improve the overall security of the studied organization.
A Dive into the Dark Web: Hierarchical Traffic Classification of Anonymity Tools

Privacy-preserving protocols and tools are increasingly adopted by Internet users nowadays. These mechanisms are challenged by the process of traffic classification (TC) which, other than being an important workhorse for several network management tasks, becomes a key factor in the assessment of their privacy level, both from offensive (malign) and defensive (benign) standpoints. In this paper, we propose TC of anonymity tools (and deeper, of their running services and applications) via a truly hierarchical approach. Capitalizing a public dataset released in 2017 containing anonymity traffic, we provide an in-depth analysis of TC and we compare the proposed hierarchical approach with a flat counterpart. The proposed framework is investigated in both the usual TC setup and its “early” variant (i.e., only the first segments of traffic aggregate are used to take a decision). Results highlight a general improvement over the flat approach in terms of all the classification metrics. Further performance gains are also accomplished by tuning the thresholds ensuring progressive censoring. Finally, fine-grain performance investigation allows us to demonstrate lower severity of errors incurred by the hierarchical approach (as opposed to the flat case) and highlight poorly classifiable services/applications of each anonymity tool, gathering useful feedback on their privacy level.
Deep CNNs With Self-Attention for Speaker Identification

Most current works on speaker identification are based on i-vector methods; however, there is a marked shift from the traditional i-vector to deep learning methods, especially in the form of convolutional neural networks (CNNs). Rather than designing features and a subsequent individual classification model, we address the problem by learning features and recognition systems using deep neural networks. Based on the deep convolutional neural network (CNN), this paper presents a novel text-independent speaker identification method for speaker separation. Specifically, this paper is based on the two representative CNNs, called the visual geometry group (VGG) nets and residual neural networks (ResNets). Unlike prior deep neural network-based speaker identification methods that usually rely on a temporal maximum or average pooling across all time steps to map variable-length utterances to a fixed-dimension vector, this paper equips these two CNNs with a structured self-attention mechanism to learn a weighted average across all time steps. Using the structured self-attention layer with multiple attention hops, the proposed deep CNN network is not only capable of handling variable-length segments but also able to learn speaker characteristics from different aspects of the input sequence. The experimental results on the speaker identification benchmark database, VoxCeleb demonstrate the superiority of the proposed method over the traditional i-vector-based methods and the other strong CNN baselines. In addition, the results suggest that it is possible to cluster unknown speakers using the activation of an upper layer of a pre-trained identification CNN as a speaker embedding vector.
Solving Imperfect-Information Games via Discounted Regret Minimization

Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfectinformation games. In this paper we introduce novel CFR variants that 1) discount regrets from earlier iterations in various ways (in some cases differently for positive and negative regrets), 2) reweight iterations in various ways to obtain the output strategies, 3) use a non-standard regret minimizer and/or 4) leverage “optimistic regret matching”. They lead to dramatically improved performance in many settings. For one, we introduce a variant that outperforms CFR+, the prior state-of-the-art algorithm, in every game tested, including large-scale realistic settings. CFR+ is a formidable benchmark: no other algorithm has been able to outperform it. Finally, we show that, unlike CFR+, many of the important new variants are compatible with modern imperfect-informationgame pruning techniques and one is also compatible with sampling in the game tree.
Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains

Implicit discourse relation classification is one of the most difficult tasks in discourse parsing.Previous studies have generally focused on extracting better representations of the relational arguments.In order to solve the task, it is however additionally necessary to capture what events are expected to cause or follow each other.Current discourse relation classifiers fall short in this respect.We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. ( 2019), which were trained on a nextsentence prediction task, and thus encode a representation of likely next sentences.The BERT-based model outperforms the current state of the art in 11-way classification by 8% points on the standard PDTB dataset.Our experiments also demonstrate that the model can be successfully ported to other domains: on the BioDRB dataset, the model outperforms the state of the art system around 15% points.
Linking artificial and human neural representations of language

What information from an act of sentence understanding is robustly represented in the human brain?We investigate this question by comparing sentence encoding models on a brain decoding task, where the sentence that an experimental participant has seen must be predicted from the fMRI signal evoked by the sentence.We take a pre-trained BERT architecture as a baseline sentence encoding model and fine-tune it on a variety of natural language understanding (NLU) tasks, asking which lead to improvements in brain-decoding performance.
A Logic-Driven Framework for Consistency of Neural Models

While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples.In this paper, we formalize such inconsistency as a generalization of prediction error.We propose a learning framework for constraining models using logic rules to regularize them away from inconsistency.Our framework can leverage both labeled and unlabeled examples and is directly compatible with off-the-shelf learning schemes without model redesign.We instantiate our framework on natural language inference, where experiments show that enforcing invariants stated in logic can help make the predictions of neural models both accurate and consistent.