Reviewerly/exHarmony huggingface dataset:
Sub-Word Parallel Precision-Scalable MAC Engines for Efficient Embedded DNN Inference

To enable energy-efficient embedded execution of Deep Neural Networks (DNNs), the critical sections of these workloads, their multiply-accumulate (MAC) operations, need to be carefully optimized.The SotA pursues this through runtime precision-scalable MAC operators, which can support the varying precision needs of DNNs in an energy-efficient way.Yet, to implement the adaptable precision MAC operation, most SotA solutions rely on separately optimized low precision multipliers and a precision-variable accumulation scheme, with the possible disadvantages of a high control complexity and degraded throughput.This paper, first optimizes one of the most effective SotA techniques to support fully-connected DNN layers.This mode, exploiting the transformation of a high precision multiplier into independent parallel low-precision multipliers, will be called the Sum Separate (SS) mode.In addition, this work suggests an alternative low-precision scheme, i.e. the implicit accumulation of multiple low precision products within the multiplier itself, called the Sum Together (ST) mode.Based on the two types of MAC arrangements explored, corresponding architectures have been proposed to implement DNN processing.The two architectures, yielding the same throughput, are compared in different working precisions (2/4/8/16-bit), based on Post-Synthesis simulation.The result shows that the proposed ST-Mode based architecture outperforms the earlier SS-Mode by up to ×1.6 on Energy Efficiency (TOPS/W) and ×1.5 on Area Efficiency (GOPS/mm 2 ).
Evaluating the effectiveness of learner controlled information security training

The goal of this study is to examine the effects of learner control on information security (ISec) training effectiveness. While organizations recognize the importance of education and training in security and invest in such efforts, the design of these programs often lacks theoretical grounding and the outcomes are often not critically evaluated. This paper attempts to fill these gaps by (1) identifying desirable characteristics for the design of such training programs, (2) using these characteristics as guidelines to design a web-based information security training (3) experimentally evaluating the effectiveness of the training using critical outcomes such as training satisfaction, security training performance, self-efficacy, perceived threat severity and susceptibility. We find that web based ISec training that incorporates learner control positively affects training reactions and learning outcomes.
The Role of "Condition"

Conditions play an essential role in scientific observations, hypotheses, and statements. Unfortunately, existing scientific knowledge graphs (SciKGs) represent factual knowledge as a flat relational network of concepts, as same as the KGs in general domain, without considering the conditions of the facts being valid, which loses important contexts for inference and exploration. In this work, we propose a novel representation of SciKG, which has three layers. The first layer has concept nodes, attribute nodes, as well as the attaching links from attribute to concept. The second layer represents both fact tuples and condition tuples. Each tuple is a node of the relation name, connecting to the subject and object that are concept or attribute nodes in the first layer. The third layer has nodes of statement sentences traceable to the original paper and authors. Each statement node connects to a set of fact tuples and/or condition tuples in the second layer. We design a semi-supervised Multi-Input Multi-Output sequence labeling model that learns complex dependencies between the sequence tags from multiple signals and generates output sequences for fact and condition tuples. It has a self-training module of multiple strategies to leverage the massive scientific data for better performance when manual annotation is limited. Experiments on a data set of 141M sentences show that our model outperforms existing methods and the SciKGs we constructed provide a good understanding of the scientific statements.
Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images

Although Faster R-CNN based text detection approaches have achieved promising results, their localization accuracy is not satisfactory in certain cases due to their sub-optimal bounding box regression based localization modules. In this paper, we address this problem and propose replacing the bounding box regression module with a novel LocNet based localization module to improve the localization accuracy of a Faster R-CNN based text detector. Given a proposal generated by a region proposal network (RPN), instead of directly predicting the bounding box coordinates of the concerned text instance, the proposal is enlarged to create a search region so that an “In-Out” conditional probability to each row and column of this search region is assigned, which can then be used to accurately infer the concerned bounding box. Furthermore, we present a simple yet effective two-stage approach to convert the difficult multi-oriented text detection problem to a relatively easier horizontal text detection problem, which makes our approach able to robustly detect multi-oriented text instances with accurate bounding box localization. Experiments demonstrate that the proposed approach boosts the localization accuracy of Faster R-CNN based text detectors significantly. Consequently, our new text detector has achieved superior performance on both horizontal (ICDAR-2011, ICDAR-2013 and MULTILIGUL) and multi-oriented (MSRA-TD500, ICDAR-2015) text detection benchmark tasks.
Touching Products Virtually: Facilitating Consumer Mental Imagery with Gesture Control and Visual Presentation

Gesture-based interaction has greatly changed the way in which we interact with online products by allowing users to control digital systems with hand movements. This study investigates how gesture-based interaction modes, namely, mid-air gesture and touchscreen gesture, compared with mouse-based interaction, affect consumers' virtual product experiences (VPE) by eliciting mental imagery (i.e., haptic imagery and spatial imagery). Furthermore, we explore how visual product presentation can be designed to facilitate different types of interaction modes. Through a lab experiment, we find that touchscreen gesture outper- forms mid-air gesture and mouse-based interaction in terms of eliciting haptic imagery, and this effect is mitigated when 3D presentation is used. We also find that mid-air gesture outperforms touchscreen gesture and mouse-based interaction in terms of eliciting spatial imagery when 3D presentation is used. Both haptic imagery and spatial imagery can further reduce consumers' product uncertainty. Our results extend prior work on interactivity design of VPE and further contribute to the emerging literature on gesture-based interaction.
AutoNE

Network embedding (NE) aims to embed the nodes of a network into a vector space, and serves as the bridge between machine learning and network data. Despite their widespread success, NE algorithms typically contain a large number of hyperparameters for preserving the various network properties, which must be carefully tuned in order to achieve satisfactory performance. Though automated machine learning (AutoML) has achieved promising results when applied to many types of data such as images and texts, network data poses great challenges to AutoML and remains largely ignored by the literature of AutoML. The biggest obstacle is the massive scale of real-world networks, along with the coupled node relationships that make any straightforward sampling strategy problematic. In this paper, we propose a novel framework, named AutoNE, to automatically optimize the hyperparameters of a NE algorithm on massive networks. In detail, we employ a multi-start random walk strategy to sample several small sub-networks, perform each trial of configuration selection on the sampled sub-network, and design a meta-leaner to transfer the knowledge about optimal hyperparameters from the sub-networks to the original massive network. The transferred meta-knowledge greatly reduces the number of trials required when predicting the optimal hyperparameters for the original network. Extensive experiments demonstrate that our framework can significantly outperform the existing methods, in that it needs less time and fewer trials to find the optimal hyperparameters.
PSRide: Privacy-Preserving Shared Ride Matching for Online Ride Hailing Systems

Online Ride Hailing (ORH) has extensively made our trip more convenient. With mobile devices, riders can request taxis through ORH systems in a short time. However, to enjoy ORH services, users need to submit their location information to ORH systems, which raises serious privacy concerns. In this paper, we study the privacy leakage of online ridesharing matching, a more complex and economy ORH service that allows riders to share rides with others, and propose a privacy-preserving shared ride matching scheme, called PSRide. PSRide can find the taxi with the minimum additional travel time to serve a new rider based on its existing schedule, while protecting the location privacy of both riders and taxis. In PSRide, we propose a zone-based minimum road travel time estimation approach and a secure comparison protocol to efficiently optimize the schedules of taxis for a new rider over encrypted data. We implement PSRide and analyze it thoroughly. Theoretical analysis and experimental evaluations show that PSRide is secure and efficient for ORH systems.
Deep Top-$k$ Ranking for Image–Sentence Matching

Image-sentence matching is a challenging task for the heterogeneity-gap between different modalities. Ranking-based methods have achieved excellent performance in this task in past decades. Given an image query, these methods typically assume that the correct matched image-sentence pair must rank before all other mismatched ones. However, this assumption may be too strict and prone to the overfitting problem, especially when some sentences in a massive database are similar and confusable with one another. In this paper, we relax the traditional ranking loss and propose a novel deep multi-modal network with a top-k ranking loss to mitigate the data ambiguity problem. With this strategy, query results will not be penalized unless the index of ground truth is outside the range of top-k query results. Considering the non-smoothness and non-convexity of the initial top-k ranking loss, we exploit a tight convex upper bound to approximate the loss and then utilize the traditional back-propagation algorithm to optimize the deep multi-modal network. Finally, we apply the method on three benchmark datasets, namely, Flickr8k, Flickr30k, and MSCOCO. Empirical results on metrics R@K (K = 1, 5, 10) show that our method achieves comparable performance in comparison to state-of-the-art methods.
Supporting Analysis of Technical Debt Causes and Effects with Cross-Company Probabilistic Cause-Effect Diagrams

Understanding TD causes can support development teams in defining actions that could be taken to prevent the occurrence of debt items. Understanding the effects of TD could aid in prioritization of TD items to pay off to minimize possible negative consequences for the project. Existing work has revealed 105 causes and 85 effects of TD, and this high number can make it difficult to make practical use of this information. Without a consolidated representation, we would need to rely on a set of tables and isolated pieces of data. In this work, we propose the use of cross-company probabilistic cause-effect diagrams to represent information about TD causes and effects. We hypothesize that such diagrams can be useful to support TD cause/effect analysis sessions and empirically investigate this issue. Results from a case study performed with 72 participants indicate that the diagrams are able to positively support the management of TD, making it easier to identify its causes and the effects of its presence. Most of the participants also agreed that, by using the proposed diagrams, they gain agility, productivity, performance, and effectiveness. Finally, 89% of the participants stated that the use of the diagrams helped them to identify causes and effects of TD that they would not have identified without their support.
BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran

Mineral prospectivity mapping (MPM) is reckoned as a lucrative tool regarding exploration programs that include lengthy and multi-branched decision pathways with variously conceivable outcomes. This paper outlines the prosperous application of a new hybrid multi-criteria decision-making (MCDM) method called BWM-ARAS for integrating multisource geological dataset to delineate highly Cu prospectivity areas in the Abhar area, NW Iran. In fact, the BWM-ARAS method is a compound of two distinct procedures, in which the BWM method is exploited for weighting to criteria and sub-criteria while ARAS method is responsible for ranking the varieties of alternatives. Herein, BWM-ARAS method is implemented on a set of multisource geoscience data as targeting criteria (derived from geological, geochemical, and remote sensing dataset) pertaining to the mineral deposit of type sought that are termed evidential maps. For fulfilling the integration process, the spatial values of evidential maps are initially transformed to a new space ranging [0, 1] by the logistic sigmoid function, and then discretized through C-A fractal-based model to ascertain thresholds corresponding favorable populations. In addition, a fuzzy MPM, a straightforward knowledge-based method, is accomplished to peruse the adequacy of the former method. The predication-area (P-A)plot and normalized density (Nd) as authoritative methods were conducted to evaluate the efficiency of abovementioned prospectivity models. Accordingly, the obtained results corroborated the higher authenticity of the BWM-ARAS model (Nd=7.33) compared with the fuzzy model (Nd=3.54) in delimiting the sought areas.
A survey on access control mechanisms for cloud computing

Abstract Cloud computing is an Internet‐based computing where the information technology resources are provided to end users following their request. With this technology, users and businesses can access programs, storage, and application development platforms through the Internet and via the services offered by the cloud service providers (CSPs). One of the biggest obstructions in the cloud computing environment is data security. Actually, the data are dispersed across multiple machines and storage devices such as servers, computers, and various mobile devices. The uncontrolled access to these resources and data leads to many important data security risks for the end users. In this way, and in order to ensure the reliability of the cloud and the trust of the users regarding this environment, controlling access to data and resources as well as protecting and ensuring their security becomes a critical task for CSPs. In this work, we present a comprehensive review of existing access control mechanisms used in the cloud computing environment. The advantages and disadvantages of each of these models are discussed and presented along with their analysis. Also, we study the cloud requirements of these models, and we evaluate existing control mechanisms against these requirements.
Exploring and exploiting the correlations between bug-inducing and bug-fixing commits

Bug-inducing commits provide important information to understand when and how bugs were introduced. Therefore, they have been extensively investigated by existing studies and frequently leveraged to facilitate bug fixings in industrial practices.
The Communication Complexity of Threshold Private Set Intersection

Threshold private set intersection enables Alice and Bob who hold sets $$S_{\mathsf {A}}$$ and $$S_{\mathsf {B}}$$ of size n to compute the intersection $$S_{\mathsf {A}} \cap S_{\mathsf {B}} $$ if the sets do not differ by more than some threshold parameter $$t$$ . In this work, we investigate the communication complexity of this problem and we establish the first upper and lower bounds. We show that any protocol has to have a communication complexity of $$\varOmega (t)$$ . We show that an almost matching upper bound of $$\tilde{\mathcal {O}}(t)$$ can be obtained via fully homomorphic encryption. We present a computationally more efficient protocol based on weaker assumptions, namely additively homomorphic encryption, with a communication complexity of $$\tilde{\mathcal {O}}(t ^2)$$ . For applications like biometric authentication, where a given fingerprint has to have a large intersection with a fingerprint from a database, our protocols may result in significant communication savings. Prior to this work, all previous protocols had a communication complexity of $$\varOmega (n)$$ . Our protocols are the first ones with communication complexities that mainly depend on the threshold parameter $$t$$ and only logarithmically on the set size n.
New Constructions of Reusable Designated-Verifier NIZKs

Non-interactive zero-knowledge arguments (NIZKs) for $$\mathsf {NP}$$ are an important cryptographic primitive, but we currently only have instantiations under a few specific assumptions. Notably, we are missing constructions from the learning with errors (LWE) assumption, the Diffie-Hellman (CDH/DDH) assumption, and the learning parity with noise (LPN) assumption. In this paper, we study a relaxation of NIZKs to the designated-verifier setting (DV-NIZK), where a trusted setup generates a common reference string together with a secret key for the verifier. We want reusable schemes, which allow the verifier to reuse the secret key to verify many different proofs, and soundness should hold even if the malicious prover learns whether various proofs are accepted or rejected. Such reusable DV-NIZKs were recently constructed under the CDH assumption, but it was open whether they can also be constructed under LWE or LPN. We also consider an extension of reusable DV-NIZKs to the malicious designated-verifier setting (MDV-NIZK). In this setting, the only trusted setup consists of a common random string. However, there is also an additional untrusted setup in which the verifier chooses a public/secret key needed to generate/verify proofs, respectively. We require that zero-knowledge holds even if the public key is chosen maliciously by the verifier. Such reusable MDV-NIZKs were recently constructed under the "one-more CDH" assumption, but constructions under CDH/LWE/LPN remained open. In this work, we give new constructions of (reusable) DV-NIZKs and MDV-NIZKs using generic primitives that can be instantiated under CDH, LWE, or LPN.
An Adaptive Online Parameter Control Algorithm for Particle Swarm Optimization Based on Reinforcement Learning

Parameter control is critical to the performance of any evolutionary algorithm (EA). In this paper, we propose a Q-Learning-based Particle Swarm Optimization (QLPSO) algorithm, which uses the Reinforcement Learning (RL) to train the parameters in Particle Swarm Optimization (PSO) algorithm. The core of the QLPSO algorithm is a three-dimensional Q table which consists of a state plane and an action axis. The state plane includes the state of the particles in both of the decision space and the objective space. The action axis controls the exploration and exploitation of particles by setting different parameters. The Q table can help particles to select actions according to their states. Besides, the Q table should be updated by reward function which is designed according to the performance change of particles and the number of iterations. The main difference between the QLPSO algorithms for single-objective and multi-objective optimization lies in the evaluation of the solution performance. In single-objective optimization, we only compare the fitness values of solutions, while in multi-objective optimization, we need to discuss the dominant relationship between solutions with the help of Pareto front. The performance of QLPSO is tested based on 6 single-objective and 5 multi-objective benchmark functions. The experiment results reveal the competitive performance of QLPSO compared with other algorithms.
List of Deep Learning Models

Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.
EIDM: A Ethereum-Based Cloud User Identity Management Protocol

In cloud system, user identity authentication is a key problem. If design defects persist in a cloud user identity authentication scheme, direct risks of sensitive data loss and severe information breach will be incurred. At present, the main problem of cloud user identity management system is that it relies too much on third-party services. Although some third-party-detachment schemes have been proposed in recent years, most of them still rely heavily on cloud server-centered design system. In this paper, a cloud user identity management protocol based on ethereum blockchain was proposed, followed by an establishment of a simple credit management system framework. The new protocol is an improved version of CIDM (Consolidated Identity Management) referred to as EIDM (Ethrerum-based Identity Management) protocol. In the improved protocol, JWT (JSON Web Token) in OAuth 2.0 was used to introduce smart contracts into EIDM protocol, and the credit management system was added to the system so that it can provide a credible identity authentication protocol for cloud users and service providers. The new protocol solves the problem of over-reliance on third parties in the existing identity management system solutions. In the end, an analysis on the security of the new protocol showed that the EIDM protocol proposed in this paper presents more diversified security guarantees relative to the CIDM protocol. The performance evaluation results also indicated that the new protocol demonstrates better practicability and flexibility.
Elmo

We present Elmo, a system that addresses the multicast scalability problem in multi-tenant datacenters. Modern cloud applications frequently exhibit one-to-many communication patterns and, at the same time, require sub-millisecond latencies and high throughput. IP multicast can achieve these requirements but has control- and data-plane scalability limitations that make it challenging to offer it as a service for hundreds of thousands of tenants, typical of cloud environments. Tenants, therefore, must rely on unicast-based approaches (e.g., application-layer or overlay-based) to support multicast in their applications, imposing bandwidth and end-host CPU overheads, with higher and unpredictable latencies.
Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features

Analyses of Software Product Lines (SPLs) rely on automated solvers to navigate complex dependencies among features and find legal configurations. Often these analyses do not support numerical features with constraints because propositional formulas use only Boolean variables. Some automated solvers can represent numerical features natively, but are limited in their ability to count and Uniform Random Sample (URS) configurations, which are key operations to derive unbiased statistics on configuration spaces.