Reviewerly/exHarmony huggingface dataset:
Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters

We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most of the proposed methods for currency denomination recognition only focus on attaining higher recognition rates, our aim is more complex, i.e., attaining a high recognition rate in the presence of image variations. Since, the aspect ratio of a currency note is invariant to such differences, an image-based recognition of currency notes based on aspect ratio is more likely to be translation- and rotation-invariant. Therefore, we adapt a two step procedure that first extracts a currency note from the homogeneous image background via local entropy and range filters. Then, the aspect ratio of the extracted currency note is calculated to determine its denomination. To validate our proposed method, we gathered a new dataset with the largest and most diverse collection of Pakistani currency notes, where each image contains either a single or multiple notes at arbitrary positions and orientations. We attain an overall average recognition rate of 99% which is very encouraging for our method, which relies on a single feature and is suited for real-time applications. Consequently, the method may be extended to other international and historical currencies, which makes it suitable for business and digital humanities applications.
Using Cultural Probes In New Contexts

Cultural probes have long been used in HCI and CSCW to provide researchers with glimpses into the local cultures for which they are designing. Researchers in HCI4D/ICTD are increasingly interested in more deeply understanding local cultures in the developing regions where they work. However, few use cultural probes in their research. I present a case study describing my experience using this subjective, design-led method in Bungoma County, Kenya. Returns from my comment cards and digital camera activities draw attention to the diversity, idiosyncrasies, and complexity of everyday life in Bungoma. I conclude that cultural probes can benefit HCI4D/ICTD research.
Weighted Multiple Instance-Based Deep Correlation Filter for Video Tracking Processing

With the development of internet technology, the video data has been widely used in multimedia devices, such as video surveillance, webcast, and so on.Lots of visual processing algorithms are developed to handle the corresponding visual task, but the challenging problems still exist.In this paper, we propose a weighted multiple instances based deep correlation filter for visual tracking processing, which utilizes the importance of instances for training of deep learning model and correlation filter.First, the initial object appearance is modeled based on the confidence of the object and background at the first frame.During the tracking, the superpixel is used to capture the object appearance variations.Most importantly, our tracker can enhance the discriminative ability of the object using deep residual network and improve the tracking efficiency with correlation filter.Second, we introduce the sample importance into residual deep learning model to improve the training performance.We define the importance of each instance by computing the sore of all the pixels within the corresponding instance.Third, we update the parameters of deep learning network and correlation filter in a fixed interval frames to reduce the object drift.Extensive experiments on the OTB2015 benchmark and VOT2018 dataset demonstrate that the proposed object tracking algorithm outperforms the state-of-the-art tracking algorithms.
FRFP: A Friend Recommendation Method Based on Fine-Grained Preference

In photography community, users are often asked and encouraged to give relevant tags based on the content of the photos when uploading them. These tags are often fine-grained and can be better used to analyze the user's fine-grained photography preferences for friend recommendation. However, the recommendation faces challenges because the latest related research works rarely pay attention to the user's photography preferences are fine-grained, which leads to poor friend recommendation. Therefore, we try to propose a new Friend Recommendation method by user's Fine-grained Preference (FRFP). Firstly, FRFP method extracts the user's fine-grained photography preference features from the perspective of the fine-grained tag. Then, we use the pagerank algorithm to calculate the importance of the preference feature tag as the score of the user-item scoring matrix, and generate a friend recommendation list through the collaborative filtering algorithm. Finally, we use user activity to weight the users in friend recommendation list, preferentially recommend users with high user activity to target user, and improve the quality of friend recommendation. The experimental results on real-word data show the effectiveness and precision of the proposed method in friend recommendation for photographers.
A Nobel Approach to Detect Edge in Digital Image Using Fuzzy Logic

Edge detection is a popular technique to find out the boundaries of different objects in a digital image. These edges are searched on the basis of gradients that are available in the image. The gradients depend upon the intensity and value of pixels. In this paper, a new technique is proposed for edge detection which is based on the fuzzy rule-based system. Fuzzy-based system requires mainly three steps, conversion of inputs to fuzzy linguistic variables, then a set of rules that can be applied on inputs and final step is again converting back from fuzzy logic output to crisp output. Horizontal and vertical gradient vectors have been considered as inputs for the fuzzy system. Gaussian membership functions and triangular membership functions are used for converting crisp inputs into fuzzy input. For defuzzification purpose centroid method is applied. The Mamdani model has been used to develop the system. The result of proposed system is better than most of the popular conventional edge detection techniques. The proposed system also minimizes the noisy details and can also be used variety of images.
ATENA

Exploratory Data Analysis (EDA), is an important yet challenging task, that requires profound analytical skills and familiarity with the data domain. While Deep Reinforcement Learning (DRL) is nowadays used to solve AI challenges previously considered to be intractable, to our knowledge such solutions have not yet been applied to EDA.
ALEXIA

Illegal use of memory pointers is a serious security vulnerability. A large number of malwares exploit the spatial and temporal nature of these vulnerabilities to subvert execution or glean sensitive data from an application. Recent countermeasures attach metadata to memory pointers, which define the pointer’s capabilities. The metadata is used by the hardware to validate pointer-based memory accesses. However, recent works have considerable overheads. Further, the pointer validation is decoupled from the actual memory access. We show that this could open up vulnerabilities in multithreaded applications and introduce new vulnerabilities due to speculation in out-of-order processors. In this article, we demonstrate that the overheads can be reduced considerably by efficient metadata management. We show that the hardware can be designed in a manner that would remain safe in multithreaded applications and immune to speculative vulnerabilities. We achieve these by ensuring that the pointer validations and the corresponding memory access is always done atomically and in order. To evaluate our scheme, which we call ALEXIA, we enhance an OpenRISC processor to perform the memory validation at runtime and also add compiler support. ALEXIA is the first hardware countermeasure scheme for memory protection that provides such an end-to-end solution. We evaluate the processor on an Altera FPGA and show that the runtime overhead, on average, is 14%, with negligible impact on the processor’s size and clock frequency. There is also a negligible impact on the program’s code and data sizes.
"Am I Never Going to Be Free of All This Crap?"

Every day on social media, people see streams of content curated by algorithms that leverage their relationships, preferences, and identities. However, algorithms can oversimplify the complexity of people's social contexts. Consequently, algorithms can present content to people in ways that are insensitive to their circumstances. Through 19 in-depth interviews, our empirical study examines instances of contextually insensitive content through the lens of people's upsetting encounters with content about their ex-romantic partners on Facebook. We characterize the encounters our participants had with content about their exes, including where on Facebook it occurred, the types of social connections involved in the content, and participants' perceptions of why the content appeared. Based on our findings, we describe the "social periphery"---the complex social networks and data that enable inferred connections around otherwise explicit relationships---and discuss the design challenges that the periphery presents designers.
An Empirical Study of Cryptographic Libraries for MPI Communications

As High Performance Computing (HPC) applications with data security requirements are increasingly moving to execute in the public cloud, there is a demand that the cloud infrastructure for HPC should support privacy and integrity. Incorporating privacy and integrity mechanisms in the communication infrastructure of today's public cloud is challenging because recent advances in the networking infrastructure in data centers have shifted the communication bottleneck from the network links to the network end points and because encryption is computationally intensive. In this work, we consider incorporating encryption to support privacy and integrity in the Message Passing Interface (MPI) library, which is widely used in HPC applications. We empirically study four contemporary cryptographic libraries, OpenSSL, BoringSSL, Libsodium, and CryptoPP using micro-benchmarks and NAS parallel benchmarks to evaluate their overheads for encrypting MPI messages on two different networking technologies, 10Gbps Ethernet and 40Gbps InfiniBand. The results indicate that (1) the performance differs drastically across cryptographic libraries, and (2) effectively supporting privacy and integrity in MPI communications on high speed data center networks is challenging-even with the most efficient cryptographic library, encryption can still introduce very significant overheads in some scenarios such as a single MPI communication operation on InfiniBand, but (3) the overall overhead may not be prohibitive for practical uses since there can be multiple concurrent communications.
Evaluation of a novel visualization for dynamic social networks

Adding temporal information to social network visualizations is still a challenging task despite previous research efforts. The dimension time is of great interest to analysts as it offers insights into various trends and patterns such as changing relationships between different actors or economic opportunities for businesses. Yet current approaches suffer from limitations that can be improved with the visualization design presented in this work. The visualization was developed considering aesthetic criteria and characteristics of adjacency matrices and node-link diagrams. In a formative evaluation process an artificial dataset was specifically created to examine dynamic social networks. A qualitative user study with observation and think-aloud protocols was conducted and analyzed with regard to the user's strategies, limitations of the visualization and potential additional features. The visualization appears to be suitable for all of the evaluated network tasks, however, path-related tasks were more challenging similar to the characteristics of matrix-based representations.
An Aircraft Detection Method Based on Improved Mask R-CNN in Remotely Sensed Imagery

Aircraft detection has become a research hotspot due to its important military and traffic status. It is very challenging since noisy background is easy to mix with the target, meanwhile, there are small and dense distributed targets in some images. This paper presents an end to end aircraft detection framework based on Mask R-CNN. First, a self-attention feature pyramid network (SA-FPN) is proposed to suppress the noise and highlight foreground. Then, in order to reduce the false alarm and increase the detection performance, we redesign the aspect ratios of the anchors. The experimental result shows that our detection method has a competitive performance.
Comparison of Machine Learning Techniques on MS Lesion Segmentation

Multiple sclerosis arises with conformational change in myelin sheath. Magnetic resonance imaging is frequently used in detection of MS. In this study, to figure out MS lesion, machine learning techniques, namely k means and support vector machine are used. K means is an unsupervised technique used to cluster data into k groups. Support vector machine is a supervised machine learning technique used as classifier. Since dataset does not contain label of images, labels are generated by pixel values adopted from original MR image. Classification results were achieved as 70.24% and 91.04% for k means and SVM respectively. According to the promising results, future research will focus on the automatization of this segmentation process via deep learning leading to medical decision support system.
A commentary on the potential impact of online communities and crime-related media on the criminal justice system: ‘do you know more? … join the investigation’

Criminological research in the media-crime nexus must critically engage with online communities focussed on influencing criminal justice outcomes. This commentary highlights the increasing diversification of such online communities and draws on the only Australian example of a media-led online wrongful conviction campaign – the Exposed Facebook group – to explore new developments in this space. This Australian example is so new that it has not yet been the subject of rigorous empirical criminological enquiry. As such, we pre-empt some of the potential impacts on the operation of the criminal justice system to generate critical thought on the issue and argue the need for further research to explore such impacts.
A Survey of Relation Extraction of Knowledge Graphs

With the widespread use of big data, knowledge graph has become a new hotspot. It is used in intelligent question answering, recommendation system, map navigation and so on. Constructing a knowledge graph includes ontology construction, annotated data, relation extraction, and ontology inspection. Relation extraction is to solve the problem of entity semantic linking, which is of great significance to many natural language processing applications. Research related to relation extraction has gained momentum in recent years, necessitating a comprehensive survey to offer a bird’s-eye view of the current state of relation extraction. In this paper, we discuss the development process of relation extraction, and classify the relation extraction algorithms in recent years. Furthermore, we discuss deep learning, reinforcement learning, active learning and transfer learning. By analyzing the basic principles of supervised learning, unsupervised learning, semi-supervised learning and distant supervision, we elucidate the characteristics of different relation extraction algorithms, and give the potential research directions in the future.
Distributed enhanced suffix arrays

Suffix arrays and trees are important and fundamental string data structures which lie at the foundation of many string algorithms, with important applications in computational biology, text processing, and information retrieval. Recent work enables the efficient parallel construction of suffix arrays and trees requiring at most O(n/p) memory per process in distributed memory.
dCCPI-predictor: A state-aware approach for effectively predicting cross-core performance interference

Multicore processors are extensively adopted in data center. Applications running on multicore processors may experience performance interference due to the contention for shared resources, which can negatively affect the Qos of online applications and reduce revenue. In order to guarantee the QoS of online applications, data center always over-provision resources for online applications, leaving a large number of cores idle, resulting in extremely low resource utilization. Improving resource utilization while ensuring the Qos of online applications is a challenge issue for data center. Most of the previous work has focused on interference prediction in fixed state mode, which affects its effectiveness in production data center. In this paper, we propose a novel interference prediction approach, namely dCCPI-predictor, which dynamically predicts the cross-core performance interference of multiple applications running together so as to identify the ’safe’ co-locations to share the server resource. dCCPI-predictor builds an interference prediction model for each application that enabling calculate the performance degradation that the application suffers in any co-location. dCCPI-predictor dynamically adapts to the state change of the application, predicting the performance interference in different states, which was overlooked in previous work. We conducted experiments on a simulated data center over multiple benchmarks to evaluate our approach. Results show that dCCPI-predictor can predict performance interference with a very high accuracy, which is greatly superior to static approach.
Classical simulation of Grovers quantum algorithm

This paper in a didactic way, presents an introduction to quantum computing where some quantum formalisms are analyzed to finally address Grover's algorithm.It is widely known that this algorithm is one of the key algorithms in quantum computing, being its ability to explode successfully the superposition principle one of the reasons.In addition, this algorithm can be used for both locating efficiently a specific element in a cluttered database and solving certain problems when it is difficult finding a proper solution, but at the same time it is very simple to try with possible candidates.Finally, a simulation of this algorithm is carried out and the results are compared with other classical algorithms to illustrate the significant potential advantages of quantum computing.
Digital Assistance for Quality Assurance

We present a digital assistance approach for applied metrology on near-symmetrical objects. In manufacturing, systematically measuring products for quality assurance is often a manual task, where the primary challenge for the workers lies in accurately identifying positions to measure and correctly documenting these measurements. This paper focuses on a use-case, which involves metrology of small near-symmetrical objects, such as LEGO bricks. We aim to support this task through situated visual measurement guides. Aligning these guides poses a major challenge, since fine grained details, such as embossed logos, serve as the only feature by which to retrieve an object's unique orientation. We present a two-step approach, which consists of (1) locating and orienting the object based on its shape, and then (2) disambiguating the object's rotational symmetry based on small visual features. We apply and compare different deep learning approaches and discuss our guidance system in the context of our use case.
Hybrid Dot-Product Calculation for Convolutional Neural Networks in FPGA

Convolutional Neural Networks (CNN) are quite useful in edge devices for security, surveillance, and many others. Running CNNs in embedded devices is a design challenge since these models require high computing power and large memory storage. Data quantization is an optimization technique applied to CNN to reduce the computing and memory requirements. The method reduces the number of bits used to represent weights and activations, which consequently reduces the size of operands and of the memory. The method is more effective if hybrid quantization is considered in which data in different layers may have different bit widths. This article proposes a new hardware module to calculate dot-products of CNNs with hybrid quantization. The module improves the implementation of CNNs in low density FPGAs, where the same module runs dot-products of different layers with different data quantizations. We show implementation results in ZYNQ7020 and compare with state-of-the-art works. Improvements in area and performance are achieved with the new proposed module.
SCC-Cryptosystem on an algebraic closure ring

The aim of this paper is to bring together two areas in which we propose a cryptosystem on a commutative ring based on the concept of integral basis in number theory. This idea goes back at least as far as [6] and [2]. We are interested in finding a commutative and associative ring denoted by ℤq[α, β] from the integral closure QL of sextic extension L = ℚ(α, β), on which we constructed a new SCC-Cryptosystem.
A Unique Value that Synthesizes the Quality Level of a Product Architecture: Outcome of a Quality Attributes Requirements Evaluation Method

The architecture can inhibit or enable the different quality attributes that guide to software product, so it is extremely important to approach the evaluation of the architecture to determine at what level the quality is being achieved. Although there are frameworks and assessment methods for the architecture or quality characteristics in particular, none of them synthesizes in a single value the level of quality of a software product. We address this shortcoming by introducing a new five-step architecture evaluation method which defines, analyze and measure the quality characteristics of a product architecture and its implementation, obtaining as a final output a unique value that represents the quality level. We illustrate the method by analyzing an architecture of a web and mobile application within the healthcare domain, developed in an agile context.
Shall we play? – Extending the Visual Analytics Design Space through Gameful Design Concepts

Many interactive machine learning workflows in the context of visual analytics encompass the stages of exploration, verification, and knowledge communication. Within these stages, users perform various types of actions based on different human needs. In this position paper, we postulate expanding this workflow by introducing gameful design elements. These can increase a user’s motivation to take actions, to improve a model’s quality, or to exchange insights with others. By combining concepts from visual analytics, human psychology, and gamification, we derive a model for augmenting the visual analytics processes with game mechanics. We argue for automatically learning a parametrization of these game mechanics based on a continuous evaluation of the users’ actions and analysis results. To demonstrate our proposed conceptual model, we illustrate how three existing visual analytics techniques could benefit from incorporating tailored game dynamics. Lastly, we discuss open challenges and point out potential implications for future research.