Reviewerly/exHarmony huggingface dataset:
A comprehensive review on collision-resistant hash functions on lattices

Hash functions have always attracted a lot of attention in modern cryptography because of their hard to invert nature. However, all previous constructions of cryptographic primitives face the threat of being broken by the recent advancements in quantum technology. The focus has thus shifted to developing cryptographic primitives on mathematical structures such as lattices that are intractable by quantum algorithms. We review the computational problems defined on lattices and their respective hardness and discuss constructions of hash function families based on both integer and ideal lattices whose security depends on these computational problems on lattices. We provide a comparative analysis of the theoretical security and concrete instantiations claimed by the different hash function families. Finally, we review techniques used in the reductions for the security proofs of constructions of different hash function families.
Going Cognitive: A Demonstration of the Utility of Task-General Cognitive Architectures for Adaptive Robotic Task Performance

It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by directly comparing a high-performing custom robotic architecture developed for the standardized robotic "FetchIt!" challenge task to a hybrid cognitive robotic architecture that allows for online one-shot task learning and task modifications through natural language instructions. The results show that there is no disadvantage of running the hybrid architecture (i.e., no significant difference in overall performance or computational overhead compared to the custom architecture) while adding the flexibility of online one-shot task instruction and modification not available in the custom architecture.
Sensitivity Analysis of Conflicting Goals in the <i>i</i>* Goal Model

Abstract Requirements engineering (RE) has been developed as a discipline to identify and then translate stakeholders’ needs into system requirements. Hence, RE is used to produce a set of specifications for developing a software system. The specifications can be applied to satisfy stakeholders and can be implemented, deployed and maintained by using their alternative design options. The past several years have seen significant improvements in RE, whereby the discipline supports the modelling and analysis of stakeholders’ goals (objectives) beyond merely incorporating these goals. Goals further help in deriving functional and non-functional requirements (NFRs) of a system. Goals play an important role in the RE process by helping elaborate the requirements. Goal-oriented requirements engineering (GORE) refers to the use of goals in RE for eliciting requirements. GORE is then used in the process of elaboration, organization, specification, analysis, negotiation, documentation and evolution of the elicited requirements. To model the software system requirements, GORE is implemented by using goals in view of goal models. Stakeholders’ goals are then represented through these goal models to assess their non-functional needs. We developed a technique for analysing conflicting goals of inter-dependent actors in a goal model. In this proposal, to ascertain stakeholders’ NFRs, we applied the cost-effectiveness analysis (CEA) to a multi-objective optimisation model in the i* goal model. This optimisation model can handle large, sophisticated systems. The requirements analyst can use information derived from the input data. The CEA further facilitates the requirements analyst by including the sensitivity of conflicting goals in the i* goal model. Based on the inter-dependency relationships, the proposed approach includes the optimisation of each objective function. This approach also uses sensitivity analysis based on the economic evaluation of derived optimal values to prioritize design options. The most cost-effective design option can hence be chosen and used to further the aim of achieving conflicting goals. This proposal uses a Telemedicine System case study, making evaluations through a simulation-based analysis.
Fined-Grained Aspect Extraction from Online Reviews for Decision Support

With the flourish of the Web 2.0, online reviews offer valuable information for customers and businesses. Deep investigation on the online reviews can help the businesses understand customers and their needs, which can assist decision making in product design and marketing. However, the massive records with irregular structure and ambiguous words pose great challenges for online review analysis. In this paper, we focus on the movie reviews and propose a framework to mine the aspect-based opinions, and utilize the results for decision making support. Based on the different sentence characteristics of movie reviews collected from Douban, the most popular movie community in China, we divide the reviews into two categories, short reviews and long reviews. Firstly, we develop different methods to extract the fine-grained aspects including the global and local aspects from the short reviews and long reviews respectively. Secondly, a lexical updating algorithm is proposed to identify the opinion words towards different aspects. In contrast to most studies that focus on determining the overall sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to mine both the various aspects and their corresponding opinions of a movie. Finally, based on the positive and negative opinions towards different aspects, the producers can improve the marketing strategy and future products. Experimental results based on the data collected from Douban verify the efficiency and accuracy of the developed methods.
Flexi Card Game: A Design Toolkit for Unconventional Communication Systems for Long-Distance Relationships

Mainstream communication technology today enables us to connect with loved ones across long distances. However, even with the proliferation of easy-to-use technology, there are reports on increasing loneliness and isolation amongst people. The technologies that enable these interactions are not necessarily designed to support meaningful emotional communication for users in long-distance relationships (LDRs). Furthermore, there is a lack of participatory tools that are designed with the focus of supporting end-user involvement in the design of LDR communication systems. We developed the Flexi Card Game (FCG), a card-based generative design toolkit to support designers and non-designers within participatory structures that can help develop unconventional communication systems to support LDRs. The FCG was developed using an iterative design process, involving end-users, designers, and researchers across five workshops with 56 participants. The paper makes three main contributions: (1) presents FCG—the novel card-based design toolkit itself, (2) describes the process of developing an LDR framework into a participatory toolkit, and (3) offers lessons and insights that can help researchers who are developing participatory tools in similar contexts.
Feedback-Guided Attributed Graph Embedding for Relevant Video Recommendation

Representation learning on graphs, as alternatives to traditional feature engineering, has been exploited in many application domains, ranging from e-commerce to computational biology. However, generating satisfactory video embeddings and putting them into practical use to improve the performance of recommendation tasks remains a challenge. In this paper, we present a video embedding approach named Equuleus, which learns video embeddings from user interaction behaviors. In Equuleus, we carefully incorporate user behavior characteristics into the construction of the video graph and the generation of node sequences. To accurately quantify the contributions of different attributes to embeddings, we propose a particular attributed encoder network, which employs an attention mechanism to aggregate different attributes in a distinguishable way. Moreover, we also leverage the user feedback as a guide to correct the generation of embeddings. Video embeddings generated by Equuleus have been used for relevant recommendation of videos in MX Player. Based on real data from MX Player, extensive offline experiments and online A/B test are conducted. Both experimental results and online CTRs illustrate that Equuleus can generate high-quality video embeddings and it can work effectively in a real-world production environment.
Is an Earthquake Felt Inside a Car?

Abstract The analysis of how an earthquake is felt was addressed with the data provided by citizens through a website dedicated to the perception of earthquakes in Italy (Data and Resources). The analysis focused on the perception of earthquakes by observers inside both parked and moving cars. These situations were compared with outdoor ones. The felt percentage of each situation was quantified for epicentral distance ranges and European Macroseismic Scale (EMS) degree. One of the main findings was the greatest sensitivity to shaking for people inside parked cars due to resonance phenomena of the automobile–observer system. The distribution of the intensity of perception in the car was analyzed as a function of the hypocentral distance and the magnitude of the earthquake. It was possible to define the attenuation trends of these intensities. The comparison of these trends with those of the equations for estimation of response spectral ordinates allowed us to have an evaluation of the frequency values of the seismic waves that caused the vibrations felt, which were found to agree with the typical frequencies of the car–observer system, as highlighted by independent studies. The results of this analysis show the possibility to include the perception of the earthquake inside a parked and moving car among the diagnostics used in the definition of macroseismic intensity degree of the EMS.
Joint Domain Adaptation Based on Adversarial Dynamic Parameter Learning

Domain adaptation aims to improve the performance of the classifier in the target domain by reducing the difference between the two domains. Domain shifts usually exist in both marginal distribution and conditional distribution, and their relative importance varies with datasets. Moreover, there is an influence between marginal distribution distance and conditional distribution distance. However, joint domain adaptation approaches rarely consider those. Existing dynamic distribution alignment methods require a feature discriminator, and they need to train a subdomain discriminator for each class. Besides, they don't think about the interaction between the two distribution distances. In this article, we propose a dynamic joint domain adaptation approach, namely Joint Domain Adaptation Based on Adversarial Dynamic Parameter Learning (ADPL), to deal with the above problems. Both marginal distribution alignment and conditional distribution alignment can be implemented by adversarial learning. The dynamic algorithm can keep a balance between marginal and conditional distribution alignment with only two domain discriminators. In addition, the dynamic algorithm takes the influence between the two distribution distances into consideration. Compared with several advanced domain adaptation methods on both text and image datasets, all classification experiments and extensive comparison experiments demonstrate that ADPL has higher learning performance of classification and less running time. This reveals that ADPL outperforms the state-of-the-art domain adaptation approaches.
The Impact of Linear Motion Blur on the Object Recognition Efficiency of Deep Convolutional Neural Networks

Noise which can appear in images affects the classification performance of Convolutional Neural Networks (CNNs). In particular, the impact of linear motion blur, which is one of the possible noises, in the classification performance of CNNs is assessed in this work. A realistic vision sensor model has been proposed to produce a linear motion blur effect in input images. This methodology allows analyzing how the performance of several considered state of the art CNNs is affected. Experiments that have been carried out indicate that the accuracy is heavily degraded by a high length of the displacement, while the angle of displacement deteriorates the performance to a lesser extent.
A Boundary-Enhanced Supervoxel Method for 3D Point Clouds

This paper presents a boundary-enhanced supervoxel method to solve over-segmentation problems in supervoxel generation of Voxel Cloud Connectivity Segmentation (VCCS). First, we use different searching methods to obtain the neighborhood of each point. Second, three variants of neighbor points are clustered by the local k-means clustering method on points directly instead of on voxels. Finally, a scale metric is used to measure the difference between two points that considers underlying 3D spatial structure of the points. Our proposed is tested on two publicly available benchmark point cloud datasets acquired by mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems, respectively. Results of the experiments show that the boundary recall approximately 7 and 4 times higher than VCCS for the best results, which our proposed methods are effective, and the cost time is feasible and effective.
Global Bitcoin Markets and Local Regulations

Since the launch of Bitcoin in 2009, the spectacular rise and fall of cryptocurrencies and the underlying blockchain technology have attracted global attention. While the application of distributed ledger technology presents great economic and business potential, significant volatility and speculative trading of cryptocurrencies have raised concerns over investor and consumer protection and prompted government interventions within their respective jurisdictions. This study focuses on the six Bitcoin trading markets comprising 99% of global trading volume as of February 2018. Adopting the event study methodology to newly compiled information about local regulation events, we find that the effect of government regulations on the Bitcoin price is only short-lived, but regulations discourage trading activities for a longer term in local markets. Interestingly, however, the repressive effect of domestic regulations on trading activities can be mitigated by the domestic financial market openness. Together, these findings are consistent with the view that Bitcoin markets are globally integrated and that, to uphold market integrity, international cooperation would be essential.
Data Imbalance in Landslide Susceptibility Zonation: A Case Study of Mandakini River Basin, Uttarakhand, India

Machine learning methods require a large amount of training data, however, the landslides do not occur everywhere and the number of landslide occurrences are limited in an area. This leads to a small number of landslide samples and a higher number of non-landslide samples. This underrepresented data and severe skewness in class distribution create an imbalance for learning algorithms, which becomes biased towards the majority class and have a low performance on the minority class. We have used two algorithms namely EasyEnsemble and BalanceCascade for reducing the imbalance in data. The balanced data is used with SVM to generate landslide susceptibility zonation maps. The results of the study show that SVM with balanced data has major improvements in the preparation of susceptibility maps over imbalanced data.
Fine-grained Data Deduplication and proof of storage Scheme in Public Cloud Storage

Verification of the integrity of the data stored on a public cloud server is a challenging research problem. While providing on-demand service to the service consumer, the cloud service provider also requires to ensure minimizing error in data stored in a cloud storage server. To address this issue, many schemes like proof of storage (POS), proof of data possession (PDP), proof of retrievability (POR) exist in the literature. However, for practical purpose, a scheme which verifies the data stored in the server should be compatible with the standard practice of data deduplication for an efficient storage system. There are a few proof of storage with data deduplication (POSD) schemes. We observe that these schemes do not consider stronger tag consistency properties and are vulnerable to duplicate data faking attacks. Moreover, existing POSD schemes are inefficient for real-world applications. In this paper, we present an efficient proof of storage scheme with data deduplication at the block-level which allows private as well as public verification of the data stored. DPoS is secure against duplicate faking attacks and unforgeable under the standard assumption. The theoretical analysis of the DPoS scheme shows that it is efficient in comparison to related schemes.
Geometric property-based convolutional neural network for indoor object detection

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.
Mixed-mode Information Flow Tracking with Compile-time Taint Semantics Extraction and Offline Replay

Static information flow analysis (IFA) and dynamic information flow tracking (DIFT) have been widely employed in offline security analysis of computer programs. As security attacks become more sophisticated, there is a rising need for IFA and DIFT in production environment. However, existing systems usually deal with IFA and DIFT separately, and most DIFT systems incur significant performance overhead. We propose MIT to facilitate IFA and DIFT in online production environment. MIT offers mixed-mode information flow tracking at byte-granularity and incurs moderate runtime performance overhead. The core techniques consist of the extraction of taint semantics intermediate representation (TSIR) at compile-time and the decoupled execution of TSIR for information flow analysis. We conducted an extensive performance overhead evaluation on MIT to confirm its applicability in production environment. We also outline potential applications of MIT, including the implementation of data provenance checking and information flow based anomaly detection in real-world applications.
Quadrotor Attitude Dynamics Identification Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

In the case of quadrotors, system identification is a challenging task because quadrotors are inherently unstable exhibit nonlinear behavior and significant coupling. In addition to this, quadrotors’ behavior is greatly influenced by characteristics and coefficients, which are very hard to measure directly or determine analytically. However, all the difficulties listed above are known to be successfully overcome by the use of artificial intelligence. In this paper, two system identification techniques were applied and compared to model quadrotor attitude dynamics. These techniques are Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and continuous-time transfer function.
Collection Development and Managment in the University Libraries of Pakistan: A Survey of Users’ Satisfaction

This study is an attempt to evaluate the effectiveness of collection development and management policies and procedures in the university libraries of North West Frontier Province of Pakistan currently Khyber Pakhtunkhwa (KPK), on the basis of users' satisfaction. A survey method was employed to conduct the research. The study covered all those well-established public and private sector universities of the province which were chartered by the Government of KPK, and recognized by HEC till December 31st, 2012 (Public= 17 and Private = 9, Total 26 universities). The population of the study were the teaching faculty of both public and private sector universities who were purposively selected at the rate of 10 teachers per university, having a minimum of three years library usage experience, knowledge of ICT applications and participation in library activities. A semi structured questionnaire was used to collect data from the target population. Out of 260 people in the sample, 200(77%) filled in questionnaires were received. The study concluded that collection development and management policies and procedures in the university libraries of KPK are ineffective in fulfilling users' information needs effectively.
Ransomware Case Studies

This chapter examines four major ransomware cases, with the first major ransomware attack in 2013 being used as a template for developing an influx of attacks since 2016. The individual case studies were chosen based on their global impact on organisations and high-profile media reports surrounding the attacks. The case study analysis process analysed the attack methodology and the outcome of each attack to determine similarities and evolutionary changes between each subsequent attack. The analysis also sought to detail the method and sophistication level of each attack, the encryption process and request for payment. These components provide the foundation for further understanding the rising threat posed by ransomware in later chapters.
Develop and evaluate an augmented reality posture training tool to promote work safety

Conventional work posture training tools included pamphlets, one-time training orientation, and/or videos. These tools did not always yield satisfactory training outcomes, and the incident rate of work-related musculoskeletal disorders did not substantially lower. In this research, modern augmented reality (AR) technology was leveraged to deliver interactive, holistic, whole-body visual information to convey safe work postures. The developmental procedure followed DMAIC by first defining specifications of training content, which led to the development of the training tool, including 3D reconstruction of a virtual instructor and building of user interface based on user-centered framework. This AR training tool was measured and analyzed through usability evaluation, and quantitative and qualitative data were obtained for cross-validation and usability issue source identification. Findings revealed the utility of 3D reconstruction of a virtual instructor and practicality of adopting conventional usability evaluation method for AR user interface usability evaluation. Feedback from the usability evaluation via questionnaire, think aloud, and post-task open-ended responses are employed to iteratively design the next version of the AR posture training tool.