Reviewerly/exHarmony huggingface dataset:
Interpolation theory for the HK-Fourier transform

We use the Henstock-Kurzweil integral and interpolation theory to extend the Fourier cosine transform operator, broadening some classical properties such as the Riemann-Lebesgue lemma.Furthermore, we show that a qualitative difference between the cosine and sine transform is preserved on differentiable functions.
Para: Harvesting CPU time fragments in Big Data Analytics

Modern data analytics typically run tasks on statically reserved resources (e.g., CPU and memory), which is prone to over-provision to guarantee the Quality of Service (QoS), leading to a large amount of resource time fragments. As a result, the resource utilization of a data analytics cluster is severely under-utilized. Workload co-location on shared resources has been substantially studied, but they are unaware the sizes of resource time fragments, making them hard to improve the resource utilization and guarantee QoS at the same time. In this paper, we propose Para, an event-driven scheduling mechanism, to harvest the CPU time fragments in co-located big data analytic workloads. Para innovates three techniques: 1) identifying the Idle CPU Time Window (ICTW) associated with each CPU core by capturing the task-switch event; 2) designing a runtime communication mechanism between each task execution of a workload and the underlying resource management system; 3) designing a pull-based scheduler to schedule a workload to run in the ICTW of another workload. We implement Para based on Apache Mesos and Spark. And the experimental results show that Para improves the CPU utilization by 44% and 30% on average relative to the original Mesos and enhanced Mesos under Spark's dynamic mode (MSDM), respectively. Moreover, Para increases the averaged task throughput of Mesos and MSDM by 4.8x and 1.7x, respectively, while guaranteeing the execution time of the primary applications.
EPI: Efficient Pointer Integrity For Securing Embedded Systems

Code-reuse attacks continue to pose a significant threat to systems security, from resource-constrained environments to data-centers. While current mitigation techniques excel at providing efficient protection for high-end devices, they typically suffer from significant performance and energy overheads when ported to the embedded domain. Thus, there is a need for developing new defenses that (1) have low overheads, (2) provide high security coverage, and (3) are especially designed for embedded devices.In this paper, we present EPI, an efficient pointer integrity mechanism that is tailored to microcontrollers and embedded devices. EPI assigns unique tags to different application assets, namely return addresses, function pointers, and data pointers to distinguish them from regular data. We then use unique memory instructions for accessing the valuable assets to prevent regular store instructions—as part of a buffer overflow vulnerability for example—from manipulating them. In order to avoid the cost of tagging the entire memory, we propose a 32-bit friendly encoding scheme to inline the tags within the application data. Using simple compiler support and minor hardware changes, we show that EPI mitigates code-reuse attacks, including the recent data-oriented programming ones. For certain modes of operation, namely return address integrity, EPI does not require compiler support, thus is applicable to legacy binaries. Our results show that EPI has 0.88% runtime overheads on the SPEC CPU2017 workloads making it viable for embedded and low-resource systems.
Research on the Computer Informatization in Multimedia Public Opinion Monitoring

Abstract This article designs and implements a Hadoop-based network public opinion monitoring system. The system uses HDFS as the underlying storage system, and builds a distributed database based on HBase on it for unified storage and management of public opinion information. The paper will introduce a Hadoop-based microblog public opinion monitoring system that can mine and analyse large-scale collected data, realize the discovery and tracking of public opinion hot topics, and the social network analysis of Weibo. The analysis results are visualized and presented for the party. Government agencies, large enterprises and other units and organizations discover sensitive information in a timely manner, grasp public opinion hotspots, grasp public opinion trends, and provide automated, systematic and scientific information support in response to public opinion crises.
Key technology research on water networking based intelligent water construction

This paper provides an in-depth analysis and study of the key technologies of water networking and intelligent water conservancy construction. Intelligent water conservancy is a change in the way all parties communicate and exchange under the conditions of the development of information technology, which to a certain extent improves the water conservancy industry. The efficiency of the work, to promote the accuracy of the information conveyed, in an era of rapid development to maintain an efficient pace of development. Intelligent water conservancy, as the name implies, has the wisdom to promote water construction based on technological innovation in water conservancy. The article investigates and analyses the impact of the application of key technologies in water conservancy by investigating and analysing the problems faced by the relevant water conservancy technologies at this stage of development. The article discusses the overall architecture of the smart water system being developed and designed by focusing on two aspects of the smart water system design and its key technologies under the general topic of smart water. and the corresponding functions were developed and implemented in practice.
Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering

Abstract Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions — e.g., optimizing aerodynamic lift. In contrast, this paper unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., pressure or electromagnetic fields) without directly specifying a set of objectives to optimize. This paper explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior. We test our contributions via the task of discovering designs that function as fluidic logic gates. We generate synthetic data that mimics fluidic devices and show that our proposed contributions better discover logic gates, as measured by adjusted Rand score. Specifically, combining our Resolution Selection preprocessing and principal component analysis resulted in the highest and tightest spread of adjusted Rand scores on our tested datasets. This opens up new avenues of research wherein computers can automatically explore different types of physics and then derive new device functions, behaviors, and structures without the need for human labels or guidance.
Neural Implicit Event Generator for Motion Tracking

We present a novel framework of motion tracking from event data using implicit expression. Our framework uses pre-trained event generation MLP called the implicit event generator (IEG) and carries out motion tracking by updating its state (position and velocity) based on the difference between the observed event and generated event from the current state estimation. The difference is computed implicitly by the IEG. Unlike the conventional explicit approach, which requires dense computation to evaluate the difference, our implicit approach realizes the update of the efficient state directly from sparse event data. Our sparse algorithm is especially suitable for mobile robotics applications in which computational resources and battery life are limited. To verify the effectiveness of our method on real-world data, we applied it to the AR marker tracking application. We have confirmed that our framework works well in real-world environments in the presence of noise and background clutter.
DeepDup: Duplicate Question Detection in Community Question Answering

Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. The identification of duplicate questions can reduce the resources required for retrieval and increase findability of the associated community forums. This ongoing study presents DeepDup, a deep learning model for duplicate question detection. Our research also explores the possibility of domain adaptation with transfer learning to improve the under-performing target domains for the text-pair duplicates classification task, using heterogeneous datasets from the Stack Exchange sub-communities for Ubuntu and English. Ultimately, our study investigates the null hypothesis that there is no significant difference between a base model and a transfer-learned model.
Search Space Reduction with Feature Selection

This chapter shows how to use feature selection to reduce the search space of genetic programming to learn scheduling heuristics for dynamic scheduling. In addition, this chapter introduces an individual mimicking strategy to generate individuals with only the selected features. Thus, the learned scheduling heuristics for dynamic scheduling contain only the selected features. The results show that the learned scheduling heuristics are competitive and have a small number of nodes and thus tend to be more interpretable. The important features for the routing rule and the sequencing rule are further analysed, and the results show that features have different importance for these two rules. In addition, the importance of features differs in different scenarios.
Device-independent and semi-device-independent entanglement certification in broadcast Bell scenarios

It has recently been shown that by broadcasting the subsystems of a bipartite quantum state, one can activate Bell nonlocality and significantly improve noise tolerance bounds for device-independent entanglement certification. In this work we strengthen these results and explore new aspects of this phenomenon. First, we prove new results related to the activation of Bell nonlocality. We construct Bell inequalities tailored to the broadcast scenario, and show how broadcasting can lead to even stronger notions of Bell nonlocality activation. In particular, we exploit these ideas to show that bipartite states admitting a local hidden-variable model for general measurements can lead to genuine tripartite nonlocal correlations. We then study device-independent entanglement certification in the broadcast scenario, and show through semidefinite programming techniques that device-independent entanglement certification is possible for the two-qubit Werner state in essentially the entire range of entanglement. Finally, we extend the concept of EPR steering to the broadcast scenario, and present novel examples of activation of the two-qubit isotropic state. Our results pave the way for broadcast-based device-independent and semi-device-independent protocols.
Quantum Radar with Vortex Microwave Photons

Quantum Orbital Angular Momentum (OAM) indicates that each Electro-Magnetic (EM) photon of an EM wave carries OAM. In the microwave band, such an EM wave photon is called a vortex microwave photon. Physical properties distinguish between EM waves with vortex and plane wave photons. When illuminating a traditional stealthy target composed of absorbing materials, a vortex microwave photon can achieve higher echo power, thereby improving the Radar Cross Section (RCS), the corresponding receiving signal power, and detection probability. Hence, the vortex microwave photon shows promise in antistealth technology. In this paper, a vortex microwave quantum radar based on the OAM quantum state is proposed. Its basic physical architecture and corresponding mathematical model are given, and the high echo power characteristics of the vortex microwave photon are analyzed using Quantum Electro-Dynamics (QED). The correctness of the theoretical calculation was experimentally verified with an approximate 9 dB improvement in echo power. Moreover, the simulations are performed to clarify the improvement in radar performance, including the receiving power and detection probability, illustrating the capability of the vortex microwave photon when applied to antistealth radar.
Using Named Entities for Recognizing Family Relationships

Named Entity Recognition problem’s objective is to automatically identify and classify entities like persons, places,organizations, and so forth. That is an area in Natural Language Processing and Information Extraction. NamedEntity Recognition is important because it is a fundamental step of preprocessing for several applications like relationextraction. However, it is a hard problem to solve as several categories of named entities are written similarly andthey appear in similar contexts. To accomplish it, we can use some hybrid approaches. Nevertheless, in this presentstudy, we use linguistic flavor by applying Local Grammar and Cascade of Transducers. Local Grammars are used torepresent the rules of a particular linguistic structure. They are often built manually to describe the entities we aimto recognize. In our study, we adapted a Local Grammar to improve the Recognition of Named Entities. The resultsshow an improvement of up to 7% on the F-measure metric in relation to the previous Local Grammar. Also, we builtanother Local Grammar to recognize family relationships from the improved Local Grammar. We present a practicalapplication for the extracted relationships using Prolog.
Mathematical modeling for cryptography using Jafari transformation method

Data protection is representing a huge trade in the modern era; the vast communication development gave this field increased attention from all sorts of parties (friends and foes); integral transforms have played a decent role in many methods that are proposed to be used in the cryptography field. In this work, the Jafari integral transform has been used as a part of a symmetric key system, by implementing a practical example in encryption and decryption; Jafari integral transform has proven its capability to be invested in cryptography and in the data security field in general.
Video Abnormal Event Detection Based on Optical Flow and 3DCNN

Abstract With the advancement of technology, the detection of abnormal events in videos has extensive theoretical and practical significance. Most events in videos are normal events, but a small number of abnormal events contain a lot of information. How to identify individual abnormal events from massive videos is the focus of concern. The paper made full use of the spatio-temporal continuity of video images. Firstly, a space-time cube was constructed by integrating optical flow information; secondly, a multi-layer three-dimensional convolutional neural network model was constructed; finally, the entire process of video abnormal event detection was realized based on this model. The experiment on the UCSD dataset showed that the method in this paper can effectively detect abnormal events in videos.
Using Machine Learning Techniques for Predicting Email Spam

The email has become one of the most efficient and cost-effective methods of communication in recent years. However, as the number of email users grows, so does the number of spam emails. Email management has become a big and rising concern for both people and companies as a consequence of its sensitivity to abuse. Spam, or the unsolicited sending of unwanted email messages, is one example of misuse. Spam is defined as unsolicited bulk email, or email sent to a large number of people without their consent. Half of the users receive 10 or more spam emails each day, while some users receive hundreds of unwanted emails per day. Online spiders are used by many spammers to discover email addresses on web pages. Because spam emails can fill up the storage space of a file server quickly, they could cause a very severe problem for many websites with thousands of users for this in this study; we present a method for spam filtering using some machine learning techniques to predict whether an email is spam or no.
Mobile Network Traffic Prediction Using High Order Markov Chains Trained At Multiple Granularity

Over the years, the need for communication networks capable of providing an ever-increasing set of services has grown. In order to satisfy user requirements and provide guarantees of reliability of the network itself, efficient techniques are required for analysis, evaluation and design. For this reason, the need arises to have models able to represent the peculiar characteristics of network traffic and to produce reliable predictions of its behavior in an adequate period of time. Therefore, network traffic prediction plays an important role by supporting many practical applications, ranging from network planning and provisioning to security. Several works so far have focused on building app-specific models. However, this choice produces multiple models that need to be properly managed and deployed across network devices. Therefore, in this paper, we explore different training strategies to reduce the number of models, adopting the Markov Chains to model mobile video apps traffic at packet-level. We discuss and experimentally evaluate the prediction effectiveness of the proposed approaches by comparing the performance of app models with models trained on a specific category of video apps and a model trained on the mix of all video traffic.
Credit Card Fraud Detection and Prevention using Machine Learning

This research focused mainly on detecting credit card fraud in real world. We must collect the credit card data sets initially for qualified data set. Then provide queries on the user's credit card to test the data set. After random forest algorithm classification method using the already evaluated data set and providing current data set[1]. Finally, the accuracy of the results data is optimised. Then the processing of a number of attributes will be implemented, so that affecting fraud detection can be found in viewing the representation of the graphical model. The techniques efficiency is measured based on accuracy, flexibility, and specificity, precision. The results obtained with the use of the Random Forest Algorithm have proved much more effective.
Keyword Extraction Methodologies Based on Rényi Entropy and Tsallis Relative Entropy

Textual and other forms of data is accumulating at a very fast pace through various social, academic and economic activities. Keyword extraction technique plays a significant role in analyzing, integrating, interpreting this amorphous data and exploiting its potential. Keyword extraction assists in making better sense of these vast information and data resources and leverage their value. In this article we pr opose and analyze keyword extraction methodology using Rényi entropy and Tsallis relative entropy. The proposed methods being statistics-based methods, these are language and domain independent method. The proposed methodology may find applications in dynamic text collection, information retrieval, natural language processing etc.
CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality. In this paper, we report the surprising empirical finding that CLIP (Radford et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references. Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements, outperforming existing reference-based metrics like CIDEr and SPICE. Information gain experiments demonstrate that CLIPScore, with its tight focus on image-text compatibility, is complementary to existing reference-based metrics that emphasize text-text similarities. Thus, we also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation. Beyond literal description tasks, several case studies reveal domains where CLIPScore performs well (clip-art images, alt-text rating), but also where it is relatively weaker in comparison to reference-based metrics, e.g., news captions that require richer contextual knowledge.
Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring

Deep Neural Networks (DNNs) are the preferred choice for image-based machine learning applications in several domains. However, DNNs are vulnerable to adversarial attacks, that are carefully-crafted perturbations introduced on input images to fool a DNN model. Adversarial attacks may prevent the application of DNNs in security-critical tasks: consequently, relevant research effort is put in securing DNNs. Typical approaches either increase model robustness, or add detection capabilities in the model, or operate on the input data. Instead, in this paper we propose to detect ongoing attacks through monitoring performance indicators of the underlying Graphics Processing Unit (GPU). In fact, adversarial attacks generate images that activate neurons of DNNs in a different way than legitimate images. This also causes an alteration of GPU activities, that can be observed through software monitors and anomaly detectors. This paper presents our monitoring and detection system, and an extensive experimental analysis that includes a total of 14 adversarial attacks, 3 datasets, and 12 models. Results show that, despite limitations on the monitoring resolution, adversarial attacks can be detected in most cases, with peaks of detection accuracy above 90%.
Forensic Photography: A Visual and Legal Record of Crime Scene

Based on priority forensic photography is a source of documenting crime scenes. Currently it enables to signify a crime scene with every significant pieces of location and evidences. Adding to forensic investigation, crime scene photography provides a true and precise record of original scene and evidences relevant to surroundings. Crime scene photograph can illustrate a crime scene easily than verbal description by an investigator as it freezes time and records the evidences. It records all type of crime scenes such as fingerprints, impressions, injuries or bruises on skin in assault cases, human identification, bloodied body and knife in murder case etc. Scientific photography skills and techniques such as UV, IR and Fluorescence light filters are practiced to discover and preserve the hidden information during investigation. It is an integral part of criminal investigation by providing proper documented focused photographs to present actual story of the scene in the courtroom.