Reviewerly/exHarmony huggingface dataset:
New Slide Attacks on Almost Self-similar Ciphers

The slide attack is a powerful cryptanalytic tool which can break iterated block ciphers with a complexity that does not depend on their number of rounds. However, it requires complete self similarity in the sense that all the rounds must be identical. While this can be the case in Feistel structures, this rarely happens in SP networks since the last round must end with an additional post-whitening subkey. In addition, in many SP networks the final round has additional asymmetries – for example, in AES the last round omits the MixColumns operation. Such asymmetry in the last round can make it difficult to utilize most of the advanced tools which were developed for slide attacks, such as deriving from one slid pair additional slid pairs by repeatedly re-encrypting their ciphertexts. Consequently, almost all the successful applications of slide attacks against real cryptosystems (e.g., FF3, GOST, SHACAL-1) had targeted Feistel structures rather than SP networks.
An Event-based Approach on Automatic Synchronous-to-Asynchronous Transformation of Web Service Invocations

One of the approaches to increase the performance of Distributed Systems, such as Service Oriented Systems, is the asynchronous invocation of the services. This is applicable because in many cases the clients do not need the result immediately and need not be blocked after the invocation. Without this mechanism, a developer should use a set of complex techniques to gain the mentioned performance. However, this procedure is both error-prone and cumbersome for the developers. Also, the state-of-the-art tools and languages which provide asynchronous mechanisms, involve the developers with the details of these invocations, as the mechanism is not transparent in these languages. In this paper, a new approach is proposed which generates an event-based middleware based on the candidate invocations available in the source/initial code. The approach also modifies the initial code to adapt with the middleware. This middleware plays the role of an interface between clients and web services. Using this approach, the invocations are transparent for the developers. This means that the synchronous and asynchronous invocations of web services are done in the same manner from the developer perspective. This is realized by automatic transformation of synchronous invocations to asynchronous ones via the generated middleware. The experimental evaluations show that the approach transforms the invocations successfully. Also, the results show that the performance of the asynchronous invocations are by far better than the synchronous ones.
POI Recommendation Based on Heterogeneous Graph Embedding

With the development and popularity of social networks, many human beings prefer to share their experiences on these networks. There are various methods proposed by the researcher which utilized user-generated content in the location-based social networks (LBSN) and recommend locations to users. However, there is a high sparsity in the user check-in information makes it tough to recommend the appropriate and accurate location to the user. To fix this issue, we put forward a proposal as a framework which utilizes a wide range of information available in these networks, each of which has its own type and provides appropriate recommendation. For this purpose, we encode the information as a number of entities and its attributes in the form of a heterogeneous graph, then graph embedding methods are used to embed all nodes in unified semantic representation space. As a result, we are able to model relations between users and venues in an efficient way and ameliorate the accuracy of the proposed method that recommends a place to a user. Our method is implemented and evaluated using Foursquare dataset, and the evaluation results depict that our work, boost performance in terms of precision, recall, and f-measure compared to the baseline work.
A Survey of Privacy-Preserving Techniques on Trajectory Data

How to protect user’s trajectory privacy while ensuing the user’s access to high quality services is the core of the study of trajectory privacy protection technology. With the rapid development of mobile devices and Location Based Service (LBS), the amount of locations and trajectories of moving objects collected by service providers is continuously increasing. On one hand, the collected trajectories contains rich spatial-temporal information, and its analysis and mining can support a variety of innovative applications. Since trajectories enable intrusive inferences which may expose private information, such as individual habits, behavioral patterns, social relationships and so on, directly publishing trajectories may result in individual privacy vulnerable to various threats. On the other hand, the existing techniques are unable to prevent trajectory privacy leakage, so the complete real-time trajectories of individuals may be exposed when they request for LBS, even if their location privacy is protected by common data protection mechanisms. Therefore, specific techniques for trajectory privacy preserving have been proposed in accordance with different application requirements. In the trajectory data publishing scenario, privacy preserving techniques must preserve data utility. In the LBS scenario, privacy preserving techniques must guarantee high quality of services. In this survey, we overview the key challenges and main techniques of trajectory privacy protection for the above requirements respectively.
Range Analysis and Applications to Root Causing

We propose a supervised learning algorithm whose aim is to derive features that explain the response variable better than the original features. Moreover, when there is a meaning for positive vs negative samples, our aim is to derive features that explain the positive samples, or subsets of positive samples that have the same root-cause. Each derived feature represents a single or multi-dimensional subspace of the feature space, where each dimension is specified as a feature-range pair for numeric features, and as a feature-level pair for categorical features. Unlike most Rule Learning and Subgroup Discovery algorithms, the response variable can be numeric, and our algorithm does not require a discretization of the response. The algorithm has been applied successfully to numerous real-life root-causing tasks in chip design, manufacturing, and validation, at Intel.
ConvERSe'20: The WSDM 2020 Workshop on Conversational Systems for E-Commerce Recommendations and Search

Conversational systems have improved dramatically recently, and are receiving increasing attention in academic literature. These systems are also becoming adapted in E-Commerce due to increased integration of E-Commerce search and recommendation source with virtual assistants such as Alexa, Siri, and Google assistant. However, significant research challenges remain spanning areas of dialogue systems, spoken natural language processing, human-computer interaction, and search and recommender systems, which all are exacerbated with demanding requirements of E-Commerce.
Towards a Kernel based Physical Interpretation of Model Uncertainty.

This paper introduces a new information theoretic framework that provides a sensitive multi-modal quantification of time series uncertainty by leveraging a quantum physical description of the projected feature space in a Reproducing Kernel Hilbert Space (RKHS). We specifically modify the kernel mean embedding, which yields an intuitive physical interpretation of the signal structure, to produce a dynamic potential field, resulting in a new energy based formulation that exploits the mathematics of quantum theory. This enables one to extract multi-scale uncertainty features of the time series in the form of information eigenmodes by utilizing moment decomposition concepts of quantum physics. In essence, this approach decomposes local time realizations of the stochastic joint process PDF in terms of quantum uncertainty moments. We specifically present the application of this framework as a non-parametric and non-intrusive surrogate tool for predictive uncertainty quantification of point-prediction neural network models, overcoming various limitations of conventional Bayesian and ensemble based UQ methods. Experimental comparisons with some established uncertainty quantification methods illustrate performance advantages exhibited by our framework.
Design and implementation of a centralized monitoring system for a remote control tower system

Due to high construction cost of traditional tower, long construction cycle, poor visual environment, blind areas in monitoring and some other problems, a remote centralized tower monitoring system came into the world. The basic idea of remote tower is aimed at changing the traditional tower management from the physical plane by transferring Long-distance Airport to centralized command center. Various surveillance data, which are collecting by the visual sensors such as high-definition cameras, are fused and processed to provide more precise and detailed control information for controllers than traditional visual surveillance towers, making full use of new technologies to assist controllers to better complete the air traffic control work of towers. This paper is mainly based on the development status and application of remote tower technology in Inner Mongolia Erlianhaote. It comprehensively reviews the basic technical characteristics of remote towers, elaborates the key technologies of each module in remote towers, and summarizes the actual cases of Erlianhaote remote towers.
A Parametric Study of Load Balancing Techniques in Cloud Environment

Cloud Computing is one of the rapidly growing areas in the field of computer science. A modern paradigm provides services through the Internet. An Internet-based technology employs pay-as-you-go model (PAYG). Load balancing is one of the important and vital issues in cloud computing. It (load balancing) is a technique which improves the distribution of workloads across various nodes. Load balancing distributes dynamic workload among various nodes so that no specific node breaks down by heavy load. It is crucial to utilize the full resources of a parallel and distributed system. Resource consumption and energy consumption both can be limited by distributing the load evenly and properly using different load balancing techniques. Lowering the use of resources increases the overall working performance of the system thus cutting down the carbon emission rate and providing the greener and safer environment. Different algorithms and techniques are employed to balance the load on nodes. These techniques can be examined on different parameters such as resource utilization, reliability of the system, system performance, related overhead of the system, power saving feature, scalability and many more. This paper presents the insight into the existing load balancing algorithms and their comparison on basis of different parameters.
Paradoxical paradigm proposals - Learning languages in mobile societies

The concept of paradigms gives us the capacity to look analytically at historical scientific and intellectual episodes in a broader framework. It does however potentially also give us the capacity to look more analytically at contemporary scientific and intellectual activity and make conjectures and predictions. This paper looks at various contemporary pedagogic paradigms, including language learning and mobile learning, and suggests both their failings and then their replacement by an over-arching pedagogic paradigm more suited to societies permeated by personal digital technologies. This might be called the mobility, learning and language paradigm. The paper uses these examples as a way of exploiting paradigmatic thinking in order to catalyse intellectual progress.
Imagination in Action

The intention of this article is to study the role of imagination in science. We are going to examine the communicative role that imagination plays in interdisciplinary scientific interaction. We are referring to that specific kind of interaction in which science is the object of research that is to a complicated situation in the contemporary science studies. We posit that the interaction between different disciplines engaged in the study of science is far from being concordant. This is especially true of the history and philosophy of science. Currently, the situation is such that, on the one hand, the philosophical reference to the historical research of science has proved being constructive in nature. On the other hand, historians remain mostly indifferent to the philosophy of science, seeking no methodological guidance from philosophers. Revealing the reasons for such an asymmetry of interests, and, as a consequence, the failure of the constructive interaction of history and philosophy of science, we analyze one hypothesis which directly refers to the work of imagination in the Kantian sense. Next, we determine that Kant's appeal to imagination opens the way for another interpretation of both the work of imagination and, as a result, the interaction of history and philosophy of science. We demonstrate why the analysis of the role of image, associated primarily with art, becomes relevant in modern research of science. Additionally, we turn to imagination, not just as a transcendental condition of knowledge but as an effective tool to organize specific research practices of interdisciplinary interaction. Therefore, an important component of our research is an appeal to a “successful” example of the synthesis of historical and philosophical research of science, which is the contemporary historical epistemology, in which one can see imagination in action.
Aztec core symbol detection method based on connected components extraction and contour signature analysis

Two dimensional bar codes are widely used in our life: retail shopping, e-ticketing, advertisement. One of the most widespread symbology (type) is Aztec Code. To read a message from Aztec Code, it first of all must be localized in the input image. To simplify this task, the bar code specification introduces a special part called Core Symbol. In current work, a topological localization method for this part is presented. It relies on a connected components extraction, contour signature analysis and uses lines estimated by the fast Hough transform. It is invariant to scaling and rotation transformations of bar code images and is able to deal with partially corrupted Core Symbols. The technique for method quality measurement is provided. As a base line we consider an algorithm with quality equal to 94.61% which follows the ISO recommendations. The obtained result for the proposed algorithm is 99.03%.
The Challenge of Collaborative IoT-Based Inferencing in Adversarial Settings

In many practical environments, resourceconstrained IoT nodes are deployed with varying degrees of redundancy/overlap–i.e., their data streams possess significant spatiotemporal correlation. We posit that collaborative inferencing, whereby individual nodes adjust their inferencing pipelines to incorporate such correlated observations from other nodes, can improve both inferencing accuracy and performance metrics (such as latency and energy overheads). However, such collaborative models are vulnerable to adversarial behavior by one or more nodes, and thus require mechanisms that identify and inoculate against such malicious behavior. We use a dataset of 8 outdoor cameras to (a) demonstrate that such collaborative inferencing can improve people counting accuracy by over 8%, and (b) show how a dynamic reputation mechanism preserves such gains even if some cameras behave maliciously.
Effect of Cognitive Load on a Random Sequence Generation Task

Executive functions are set of processes of human cognition which coordinate information, schedule actions and control attention. Random sequence generation is a crucial marker of executive functioning. The inability to generate random sequences can reveal a lot about the underlying impairments, if any. In this paper, a cognitive load inducing task is paired with a random sequence generation task resulting in a dual task paradigm. The participants are asked to add few numbers mentally and simultaneously generate a random sequence. Eye tracker has been used in the present study to understand the behavioral aspects of the stimulus. The comparisons of single and the dual task conditions showed that the cognitive load demanding task disrupted the overall performance as same attentional resources are used by both the tasks. Thus, cognitive load has huge effect on performance of random sequence generation task. This fact is well supported by introducing a measure called the load index which can be used to quantity the load induced by a cognitive load demanding task. The measure is derived based on the pupillary responses. Results confirm that the proposed measure is a reliable indicator of the load imposed on an individual. The proposed measure can be used for assessing the cognitive control while executing a complex task.
Single object detection to support requirements modeling using faster R-CNN

Requirements engineering (RE) is one of the most important phases of a software engineering project in which the foundation of a software product is laid, objectives and assumptions, functional and non-functional needs are analyzed and consolidated. Many modeling notations and tools are developed to model the information gathered in the RE process, one popular framework is the iStar 2.0. Despite the frameworks and notations that are introduced, many engineers still find that drawing the diagrams is easier done manually by hand. Problem arises when the corresponding diagram needs to be updated as requirements evolve. This research aims to kickstart the development of a modeling tool using Faster Region-based Convolutional Neural Network for single object detection and recognition of hand-drawn iStar 2.0 objects, Gleam grayscale, and Salt and Pepper noise to digitalize hand-drawn diagrams. The single object detection and recognition tool is evaluated and displays promising results of an overall accuracy and precision of 95%, 100% for recall, and 97.2% for the F-1 score.
Image resizing enhancement with DCT coefficients

According to the principle of scale transformation, signal expansion in the time domain corresponds to compression in the frequency domain, so that information energy is concentrated in the low frequency part. In this paper, an image enlargement algorithm based on the Discrete Cosine Transform (DCT) is proposed, which preserves the low frequency of the image and combines the corresponding enhancement coefficients to realize the resizing operation in the DCT domain. This paper also reach to proving of the enhancement value determinant. Then comparing the experiment on the scaled image with other interpolation algorithms, the result shows that our algorithm performs better than other methods. This method can also be carried out during DCT transformation, and is easier to implement than other methods.
Functional Privacy-preserving Outsourcing Scheme with Computation Verifiability in Fog Computing

Fog computing has become a popular concept in the application of internet of things (IoT).With the superiority in better service providing, the edge cloud has become an attractive solution to IoT networks.The data outsourcing scheme of IoT devices demands privacy protection as well as computation verification since the lightweight devices not only outsource their data but also their computation.Existing solutions mainly deal with the operations over encrypted data, but cannot support the computation verification in the same time.In this paper, we propose a data outsourcing scheme based on an encrypted database system with linear computation as well as efficient query ability, and enhance the interlayer program in the original system with homomorphic message authenticators so that the system could perform computational verifying.The tools we use to construct our scheme have been proven secure and valid.With our scheme, the system could check if the cloud provides the correct service as the system asks.The experiment also shows that our scheme could be as effective as the original version, and the extra load in time is neglectable.
Implementation of Tri-N (Niteni-Nirokke-Nambahi) and PPK (Strengthening of Character Education) in Explanation Text Learning Development of Grade 8th

This paper focuses on how the development of Tri-N and PPK in the VIII grade explanatory text learning tool in Junior High Schools. The learning tool is in the form of a Rencana Pelaksanaan Pembelajaran (RPP). Tri-N based teaching ( Niteni-Nirokke-Nambahi ) is implemented in the introduction, core activities, and closing activities in the learning process. In addition to implementing Tri-N teachings, the RPP also includes Strengthening Character Education (PPK). PPK is an educational program in schools that aims to strengthen the character of students through alignment of thought, processing, and processing of the heart. The PPK contained in the explanatory text lesson plan includes disciplines, confidence, and cooperation. The Tri-N and PPK teachings are made explicit in the lesson plans in the VIII grade explanatory text learning activities section in junior high schools.
FPGA-Based Object Detection for Autonomous Driving System

Autonomous driving systems require a real-time detection of the objects including pedestrians and obstacles on the roads. Object detection by image processing is a popular approach for autonomous driving systems. However, there are complex reflections caused by multiple light sources and objects on the roads, which disturb the robust and real-time object detection. This paper describes an FPGA-based object detection method for autonomous driving systems using multiple CMOS cameras. Our system is implemented on Xilinx Zynq-7020 SoC-FPGA, in which real-time processing of tracking white-lines for lane-keeping and obstacles/pedestrians detection on the roads are executed by the hardware on the Programmable Logics, and the whole system is controlled by a software on the Processing System (CPU) written in Python language.
DrawingPresence

One of the main measures to evaluate a head-mounted display (HMD) based experience is the state of feeling present in virtual reality (VR). The detection of disturbances of such an experience that occur over time, namely breaks in presence (BIP), enables the evaluation and improvement of these. Existing methods do not detect BIPs, e.g., questionnaires, or are complex in their application and evaluation, e.g., physiological and behavioral measures. We propose a handy post-experience method in which users reflect on their experienced state of presence by drawing a line in a paper-based drawing template. The amplitude of the drawn line represents the state of presence of the temporal progress of the experience. We propose a descriptive model that describes temporal variations in the drawings by the definition of relevant points over time, e.g., putting on the HMD, phases of the experience, transition into VR, and parameters, e.g., the transition time. The descriptive model enables us to objectively evaluate user drawings and represent the course of the drawings by a defined set of parameters. Our exploratory user study (N = 30) showed that the drawings are very consistent between participants and the method is able to securely detect a variety of BIPs. Moreover, the results indicate that the method might be used in the future to evaluate the strength of BIPs and to reflect the temporal course of a presence experience in detail. Additional application examples and a detailed discussion pave the way for others to use our method. Further, they serve as a motivation to continue working on the method and the general understanding of temporal fluctuations of the presence experience.