Reviewerly/exHarmony huggingface dataset:
Intensive Image Malware Analysis and Least Significant Bit Matching Steganalysis

Malware as defined by Kaspersky Labs is "a type of computer program designed to infect a legitimate user's computer and inflict harm on it in multiple ways." The exponential growth of the internet has led to a significant escalation in malware attacks which affect the multitude often leading to disastrous consequences. One of the most minacious methods of malware unfurling is through images. In this paper we analyze the following methods of embedding malicious payloads in images: 1) Disguising PHP/ASM web shells inside Exchangeable Image File Format i.e. EXIF data of an image. 2) An injection vulnerability that conceals Cross-Site Scripting (XSS) in the EXIF data to execute malicious payloads when the image is uploaded to a browser. 3) Feigning a malicious executable file in a zipped .sfx file format as an 4) Splitting the attack payload into safe decoder and pixel encoded code. 5) Least Significant Bit (LSB) Matching Steganography technique used for pernicious payload embedding in image pixel data. After extensive analysis of these malware embedding techniques, we present `AnImAYoung', an image malware analysis framework that thoroughly examines given images for the presence of any kind of anomalous content.Our framework utilizes ensemble methods to detect miniature statistical changes in images using machine learning, where the LSB Matching Steganography technique was used for payload embedding, which increases the accuracy of the framework. The framework achieves excellent performance by applying sophisticated computing algorithms and can be easily integrated with organizations working with Big Data providing them with a robust malware security option. This study describes the need and a practical approach to tackle this novel method of malware dissemination.
AutoFlow: Hotspot-Aware, Dynamic Load Balancing for Distributed Stream Processing

Stream applications are widely deployed on the cloud. While modern distributed streaming systems like Flink and Spark Streaming can schedule and execute them efficiently, streaming dataflows are often dynamically changing, which may cause computation imbalance and backpressure. We introduce AutoFlow, an automatic, hotspot-aware dynamic load balance system for streaming dataflows. It incorporates a centralized scheduler that monitors the load balance in the entire dataflow dynamically and implements state migrations correspondingly. The scheduler achieves these two tasks using a simple asynchronous distributed control message mechanism and a hotspot-diminishing algorithm. The timing mechanism supports implicit barriers and a highly efficient state-migration without global barriers or pauses to operators. It also supports a time-window based load-balance measurement and feeds them to the hotspot-diminishing algorithm without user interference. We implemented AutoFlow on top of Ray, an actor-based distributed execution framework. Our evaluation based on various streaming benchmark datasets shows that AutoFlow achieves good load-balance and incurs a low latency overhead in a highly data-skew workload.
Fusing Local and Global Features for Person Re-identification Using Multi-stream Deep Neural Networks

The field of person re-identification remains a challenging topic in video surveillance and public security because it is facing many problems related to the variations of the position, background and brightness scenes. In order to minimize the impact of those variations, we introduce in this work a multi-stream re-identification system based on the fusion of local and global features. The proposed system uses first a body partition segmentation network (SEG-CNN) to segment three different body regions (the whole body part, the middle and the down body parts) that will represent local features. While the original image will be used to extract global features. Second, a multi-stream fusion framework is performed to fuse the outputs of the individual streams and generate the final predictions. We experimentally prove that the multi-stream combination method improves the recognition rates and provides better results than classic fusion methods. In the rank-1/mAP, the improvement is of $$7,24 \%$$ /9, 5 for the Market-1501 benchmark dataset.
Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.
On Teaching of Computer Networks using Novel Methodologies

In this study, we focus on the methodologies that were created as a part of the national IT Academia project. We describe the standardized format that each methodology follows, making each methodology quickly understandable for the instructors. As another goal, we deploy the methodologies on the course taught at the local university. The created methodologies have proven to supply adequate output, both for the instructors and students. Outputs of the testing yield positive feedback from the side of students and render methodologies usable in the real practice, as is also proven by the feedback from the secondary school teachers who teach Computer networks courses.
Identity-based Encryption from the Diffie-Hellman Assumption

We provide the first constructions of identity-based encryption and hierarchical identity-based encryption based on the hardness of the (Computational) Diffie-Hellman Problem (without use of groups with pairings) or Factoring. Our construction achieves the standard notion of identity-based encryption as considered by Boneh and Franklin [CRYPTO 2001]. We bypass known impossibility results using garbled circuits that make a non-black-box use of the underlying cryptographic primitives.
LADNet: An Ultra-Lightweight and Efficient Dilated Residual Network With Light-Attention Module

Image classification task is an important branch of computer vision. At present, most of the mainstream CNNs are large in size and take up too much computing resources. The quality-price ratio is not satisfying when the heavy CNNs are used in image classification. So, this work proposes a spatial and channel hybrid attention module (Light-Attention module), an ultra-lightweight but efficient attention module. Given an intermediate feature map, the Light-Attention module will firstly derive the most important attention maps of the channels automatically with global stochastic pooling and Multilayer Perceptron (MLP). Then, the residual structure of Light-Attention module helps to repeatedly introduce global information for secondary screening. For better performance, our pioneering Max module and Mean module to extract key spatial features on the basis of previous operations. In the process of extracting the key attention map, LADNet uses the most labor-saving operation and save a lot of network parameters. The Light-Attention module can be seamlessly integrated into any network, while its parameters and required computing resources are both very small. We verified the excellent performance of the Light-Attention module in extracting key image information through ablation experiments on the Cifar-10, Cifar-100 and ImageNet datasets. For the experiment in Cifar-10, the image classification accuracy achieves 98.7%, achieves 96.5% for the Cifar-100, and for the ImageNet, achieves 83.9%. With the Light-Attention module, LADNet reduces the parameters of convolution neural network to 71% of the original. Compared to the other CNNs used in image classification task, the Light-Attention module uses the least parameters but scores most advanced achievements. The experiments proved that the combination of DRN (Dilated Residual Network) and Light-Attention module achieves superior performance.
Endogenizing Epistemic Actions

Through a series of examples, we illustrate some important drawbacks that the action model logic framework suffers from in its ability to represent the dynamics of information updates. We argue that these problems stem from the fact that the action model, a central construct designed to encode agents’ uncertainty about actions, is itself effectively common knowledge amongst the agents. In response to these difficulties, we motivate and propose an alternative semantics that avoids them by (roughly speaking) endogenizing the action model. We discuss the relationship between this new framework and action model logic, and provide a sound and complete axiomatization of several new logics that naturally arise.
Overview of the development of speaker recognition

Abstract Speaker recognition is a technology that recognizes a person’s identity through a person’s voice. Speaker recognition is one of the important research topics in the field of biometrics. This article first introduces the background and research significance of speaker recognition; secondly, it briefly introduces speaker recognition technology; again introduces the method research used in speaker recognition; and finally analyzes the research difficulties and challenges of speaker recognition.
Video Tampering Detection based on High-Frequency Features using Machine Learning

In recent times, security cameras can be found installed in various places such as lobbies of buildings, urban areas, and drive recorders in automobiles. Similarly, smartphones and drive recorders have become a part of our everyday lives. However, in recent years, advances are being made in technology that enables images and videos to be edited easily. For example, editing software, such as Photoshop, After Effects, and Premiere Pro, allow anyone to edit videos and images easily. Thus, it is easy for people to take pictures or videos using smartphones or drive recorders and tamper with them. When an accident or crime occurs, videos or images from security cameras can serve as legal evidence for the genuineness of the background or object. Therefore, it is necessary to investigate the possibility of tampering with the video contents for malicious purposes. In this study, we propose a method for detecting video tampering based on high-frequency features using machine learning. Artifacts of tampered videos can be detected in consecutive frames through video forgery detection. In addition, although machine learning in this field is limited by its inability to acquire sufficient manipulated data, we demonstrate that this can be improved.
Embroidered Inflatables: Exploring Sample Making in Research through Design

This paper reflects on the experience of sample making to develop interactive materials. Sample making is a way to explore possibilities related to different materials techniques. In recent years design research has put an increasing emphasis on making as a mode of exploration, which in turn has made such exploration an increasingly popular and effective design research approach. However, sample making is a messy and complex process that is hard to document and communicate. To mitigate this, design researchers typically report their journeys from the perspective of their success, retroactively editing out or reducing the accounts of experiments that did not directly contribute to their goal. Although it is a useful way to of contextualizing a design process, it can contribute to a loss of richness and complexity of the work done along the way. Samples can be seen as instantiations of socio-techno systems of production, which means that they can be looked at from different perspectives and can potentially become the starting points of new design explorations. In recognition of this quality, we aim to investigate ways that samples can be appropriated in future journeys. To do so, we analyzed and reflected on the sample making process of the Embroidered Inflatables as a design case. The project resulted in 27 samples that explored distinct challenges related to designing actuators for soft wearables through the combination of silicone casting and embroidery techniques. To explore the potential of sample appropriation, we invited a fashion designer to a creative session that analyzed these samples from her personal perspective to identify new design directions. We detail the design process, reflect on our sample making experience and present strategies to support us in the process of reevaluating and appropriating samples.
Fine-Tuning Language Models For Semi-Supervised Text Mining

The dimensionality of traditional text representation is large, but the underlying text data is sparse. This makes text clustering a very challenging task. Using language models and deep contextualized representations is promising in many Natural Language Processing (NLP) tasks. However, some task-specific guidance is necessary to adapt language models to a novel domain or to particular downstream tasks. We present an empirical study of a pipeline for semi-supervised text clustering tasks. Our proposed method utilizes a small number of labeled samples to fine-tune pre-trained language models. This fine-tuning step adapts the language models to produce task-specific contextualized representations, improving the performance of downstream text clustering tasks. We evaluate two clustering algorithms using the output of three different language models on six real-world text mining tasks to demonstrate to what extent this pipeline can improve text clustering accuracy and the amount of labeled samples needed for improvement. Our experiments show that for topic mining in novel domains or surfacing the intentions of abstracts, language models begin to produce better task-specific representations using a labeled subset as small as 0.5% of the task data. On the other hand, to find topics in domains that are overlapping with pre-training corpora, language models need labeled subsets closer to 1.0% of the task data to overcome the catastrophic forgetting problem. Further experiments show the downstream clustering accuracy gain begins to slow down or plateau if language models are fine-tuned with more than 5% of the task data. There is a trade-off between the desired downstream clustering quality and the cost for labeling and fine-tuning language models. Fine-tuned with 2.5% of the task data, our approach matches or exceeds the current state-of-the-art for several clustering tasks and provides baseline results for two novel clustering tasks. These results provide solid guidance to utilize powerful language models for text clustering and information retrieval practitioners.
Ethical Principles for Design

The paper explores ethical aspects of design as a specific field of practice. It develops a set of ethical principles for design against the theoretical backdrop of moral philosophy and the specific characteristics of design as a creative, function-oriented field. The paper provides a systematic ethical approach and critically reflects on ethical assumptions of various design approaches, such as user-centered design, green design, and design for societal values. It considers the complex ethical aspects of design in the 21st century and discusses the role of design in addressing ethical challenges of globalization and sustainability. The paper suggests an encompassing perspective on contemporary design ethics that includes users, stakeholders, society, nature, people around the world, and future generations.
Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference

In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is required to model the interaction between logic relations and numerical values in many real applications. In this paper, we propose a probabilistic inference framework, called the Numerical Markov Logic Network (NMLN), to enable efficient inference of hybrid knowledge involving both logic and arithmetic expressions. We first introduce the hybrid knowledge rules, then define an inference model, and finally, present a technique based on convex optimization for efficient inference. Built on decomposable exp-loss function, the proposed inference model can process hybrid knowledge rules more effectively and efficiently than the existing MLN approaches. Finally, we empirically evaluate the performance of the proposed approach on real data. Our experiments show that compared to the state-of-the-art MLN solution, it can achieve better prediction accuracy while significantly reducing inference time.
Combining Multiple Implicit-Explicit Interactions for Regression Analysis

Regression analysis is one of the most widely used data analysis methods, and it is increasingly important to obtain accurate results from it. To obtain accurate prediction results of regression analysis through machine learning, we must select appropriate features and train various feature interactions. The combinatorial model consists of a combination of various subordinate components and is used for automatic training of various feature interactions. However, existing combinatorial models are inefficient b ecause t hey c an t rain o nly limited feature interactions and must combine several components. To overcome these limitations, this study proposes a new model called eXtreme Interaction Network (XIN). XIN can automatically learn various explicit interactions, various levels of implicit higher-order interactions, and polynomial features. We compared the proposed XIN with existing models using four datasets with different characteristics to demonstrate that the proposed model has higher performance and lower or comparable time and space complexities. Furthermore, we conducted experiments while changing the various hyper-parameters of the XIN and demonstrated the improved performance of the proposed method in various environments.
Energy Consumption Comparison of a Novel Parallel Tracker and Its Corresponding Serial Tracker

A novel two-axis solar tracker with parallel mechanism is proposed in this paper. A dynamic model is derived by using the virtual work principle and the consumed energy including the mechanical energy and motor energy loss is computed. Taking Beijing as the working location of the solar tracker, the energy consumptions of the parallel solar tracker and its corresponding serial solar tracker are compared based on the premise that the proposed solar tracker and its corresponding serial solar tracker have similar static stiffness. Mechanical energy consumption of the proposed tracker is reduced by 7.55% compared to the serial solar tracker. The motor energy loss of the parallel solar tracker is also significantly lower. This simple and low-energy consumption solar tracker is a good alternative to the traditional solar tracker with large energy consumption.
Online Classification of RTC Traffic

Real-time communication (RTC) platforms have become increasingly popular in the last decade, together with the spread of broadband Internet access. They are nowadays a fundamental means for connecting people and supporting the economy, which relies more and more on forms of remote working. In this context, it is particularly important to act at the network level to ensure adequate Quality of Experience (QoE) to users, where proper traffic management policies are essential to prioritize RTC traffic. This, in turn, requires in-network devices to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify, in real-time, the media streams generated by RTC applications encapsulated in Secure Real Time Protocol (SRTP) flows. Using carefully tuned features extracted from packet characteristics, we train a model to classify streams into an ample set of classes, including media type (audio/video), video quality and redundant streams. To validate our approach, we use traffic from more than 88 hours of multi-party meeting calls made using the Cisco Webex Teams application. We reach an overall accuracy of 97% with a light-weight decision tree model, which makes decisions using only 1 second of traffic.
Noise detection with spectator qubits and quantum feature engineering

Designing optimal control pulses that drive a noisy qubit to a target state is a challenging and crucial task for quantum engineering. In a situation where the properties of the quantum noise affecting the system are dynamic, a periodic characterization procedure is essential to ensure the models are updated. As a result, the operation of the qubit is disrupted frequently. In this paper, we propose a protocol that addresses this challenge by making use of a spectator qubit to monitor the noise in real-time. We develop a machine-learning-based quantum feature engineering approach for designing the protocol. The complexity of the protocol is front-loaded in a characterization phase, which allow real-time execution during the quantum computations. We present the results of numerical simulations that showcase the favorable performance of the protocol.
Tracking Tangible Asset Ownership and Provenance with Blockchain

Blockchain transactions are recorded in a shared and append-only repository that multiple parties verify, validate, and agree-upon. While initially used to keep track of digital assets, blockchains now track the ownership and provenance of tangible assets. An inherent challenge in using blockchain for this task involves keeping the status of a tangible asset in the physical world in sync with its non-fungible token on a blockchain. While several blockchains are already being used in this manner, specific implementation details are fragmented. In response, this study examines four stages of tracking tangible assets using a consortiumâ€™s permissioned blockchain, including: design and governance of a blockchain, asset creation, asset transfer, and asset retirement. Based on this analysis, this study proposes a framework of risk considerations and control objectives to evaluate the extent to which a unique blockchain serves as a reliable transaction repository for tracking the ownership and provenance of tangible assets.