Reviewerly/exHarmony huggingface dataset:
SafeController: Efficient and Transparent Control-Flow Integrity for RTL Design

Control Flow Integrity (CFI) enforcement is traditionally performed to allow the permitted execution of a control flow graph (CFG). There exists several software-and hardware-assisted approaches to raise the bar against control-flow attacks and the success of different implementation techniques largely depends on precise control-flow extraction. These CFI mechanisms provide limited security due to imprecise CFG and are of limited practicality due to large overhead. On the contrary, High-Level Synthesis (HLS) performs several behavior preserving transformations on control and data flowgraph (CDFG) and generates optimized Register Transfer Level (RTL) design (datapath and controller). Due to the distributed electronics supply chain and lack of centralized control, there is no tight security policy to protect control-flow information in RTL design. In this paper, we present Safe-Controller, which provides two complementary, transparent, and strong security policies for the RTL design. Our approach performs static analysis of controller followed by lightweight instrumentation such that CFI is enforced in-place and at runtime, the modified controller follows conservative CFG with the help of a monitor. We have implemented and evaluated SafeController in several HLS benchmarks under three design corners. Experimental results show that our technique incurs negligible overhead while assuring the integrity of the controller's CFG.
On Sensitive Minima in Margin-Based Deep Distance Learning

This paper investigates sensitive minima in popular deep distance learning techniques such as Siamese and Triplet networks. We demonstrate that standard formulations may find solutions that are sensitive to small changes and thus do not generalize well. To alleviate sensitive minima we propose a new approach to regularize margin-based deep distance learning by introducing stochasticity in the loss that encourages robust solutions. Our experimental results on HPatches show promise compared to common regularization techniques including weight decay and dropout, especially for small sample sizes.
Video Analysis and Natural Language Description Generation System

The project revolves around the idea of scene understanding purpose based on the video input, thus not continuously monitoring the feed manually. The videos are extracted into the form of raw video frames and using 2D-3D CNN, the feature vector is extracted. Using You Only Look Once - version 3 (YOLOv3) algorithm, the objects present in a particular frame is identified. Also, the count of the objects is stored. The pose of people present in the frames is estimated for identification of movements. Through this, the actions are recognized as being performed by the people. All the words that are formed through the above three methods count to input to the LSTM cell. This cell selects the words based on their probabilities and confidence rate and forms a natural language sentence for the user to understand. Finally, the generated output can be modified or changed completely by the user using Human-in-the-loop concept, if required. The machine will retrain itself based on this input and generate better results next time. The central model is capable of identifying as well as discriminating between types of elements which are required for this project. This project was built as a continuation of the previous system, which works on object identification from live video input from drones. In the case of poor network issues, when sending video data becomes difficult, the data is sent in textual format.
Abnormal Interference Recognition Based on Rolling Prediction Average Algorithm

Aiming at the problems that the traditional camera abnormal interference recognition methods have a single identification type, the low recognition accuracy and reliability, the generalization ability is not strong due to the prediction of flicker. A method for camera abnormal interference recognition based on the rolling prediction average algorithm is proposed. The core ideas of this method include the following steps. Firstly, the ImageNet pre-trained ResNet50 network on the self-built abnormal interference image training set is fine-tuned. Then a model for camera abnormal interference image classification and recognition is trained. Finally, the rolling prediction average algorithm is applied to the model to realize the classification and recognition of the camera abnormal interference video online or offline. The experimental results on the self-built abnormal interference image verification set show that the proposed method can correctly identify the normal images, occlusion images, blurred images and the camera rotated images. The recognition accuracy reaches 97%. Test accuracy reaches 95% when using test set videos test, which fully verifies the feasibility and effectiveness of the proposed method. Compared with other abnormal interference recognition methods, not only the accuracy rate is high, but also the false recognition rate and missed detection rate are relatively low. The proposed method can be applied to actual monitoring scenarios to assist the work of monitoring center staff.
Limiting Privacy Breaches in Average-Distance Query

Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.
News Articles and Facts Modeling: Linked Data or Linked Fact

A press article is a collection of statements (opinions and/or allegations) that are a priori non-factual (the facts), i.e. an opinion on a specific subject. This article seeks to address the challenging issue of modeling news articles and facts. The authors also discuss the possibilities of modeling networks of factual articles based on the RED Model and Linked Data.
Implementation of Deep Learning Model for Video Colorization

Technology sometimes enhances the art and also Vandalizes. Colorizing black and white films is a very old idea dating back to 1902. For decades many movie creators opposed the idea of colorizing their black and white movies and thought of it as vandalism of their art. Today it is accepted as an enhancement to the art form. The technology itself has moved from painstaking hand colorization to today's largely automated techniques. In the last few years, with the Deep Learning revolution, automation in colorization has taken a huge leap forward. In this project, one such Deep Learning model for video colorization will be proposed and present a simple colorization methodin which the model performs object detection and imparts color based on the trained dataset to the input feed. A machine learning model have been built to automatically turn grayscale video feed into a colored video feed based on a specific dataset.
Optimal Resource Allocation for Crowdsourced Image Processing

Crowdsourced image processing has the potential to vastly impact response timeliness in various emergency situations. Because images can provide extremely important information regarding an event of interest, sending the right images to an analyzer as soon as possible is of crucial importance. In this paper, we consider the problem of optimally assigning resources, both local (CPUs in phones) and remote (network-based GPUs) to mobile devices for processing images, ultimately sending those of interest to a centralized entity while also accounting for the energy consumption. To that end, we use the Network Utility Maximization (NUM) framework, coupled with a hit-ratio estimator and energy costs, to enable a distributed implementation of the system. Our results are validated using both synthetic simulations and real-life traces.
Spinning gold from straw - evaluating the flexibility of data centres on power markets

Abstract Data centres have been the focus of research as candidates for demand response or other demand side management programs for quite some time. However, a complete framework optimising data centre demand response is still missing. This is due to the complexity of integrating more than one power flexibility technology and more than one market for power flexibility. In the presented work, this challenge is solved by creating a microeconomics inspired optimisation approach that takes the view of a data centre offering power flexibility as a ’product’ to explicit and/or implicit demand response power flexibility markets. This generic framework is then instantiated in a linear optimisation problem that optimises the power flexibility of a German High Performance Computing Centre on a set of different power flexibility markets in Germany. It is consequently shown that, under the described scenario, frequency scaling should be preferred to temporal workload shifting and that the EPEX day ahead market is the most beneficial power flexibility market.
X-VS: Crossbar-Based Processing-in-Memory Architecture for Video Summarization

Video summarization techniques identify the most interesting frames in a video based on their uniqueness/importance or relevance to a user query. Deep learning based automated video summarization techniques have gained significant importance due to the growing need to analyze the exploding video data from user devices, surveillance cameras, and social media platforms etc. In contrast to the image classification, object detection tasks which predominantly use convolutional neural networks (CNNs), video summarization techniques comprise a pipeline of more diverse networks such as text processing networks, attention and content similarity mechanisms. In this work, we present X-VS, a ReRAM processing-in-memory (PIM) hardware accelerator architecture for video summarization workloads. We augment a baseline ReRAM CNN accelerator with a systolic array-based crossbar architecture to incorporate efficient support for recurrent neural networks, attention and content similarity mechanisms and hash-based word embedding lookup to support the video summarization networks. The proposed architecture achieves an average speedup of '450x, and energy savings of '1600x for two state-of-the-art video summarization networks over CPU and GPU implementations.
Towards Active Learning Interfaces for Multi-Inhabitant Activity Recognition

Semi-supervised approaches for activity recognition are a promising way to address the labeled data scarcity problem. Those methods only require a small training set in order to be initialized, and the model is continuously updated and improved over time. Among the several solutions existing in the literature, active learning is emerging as an effective technique to significantly boost the recognition rate: when the model is uncertain about the current activity performed by the user, the system asks her to provide the ground truth. This feedback is then used to update the recognition model. While active learning has been mostly proposed in single-inhabitant settings, several questions arise when such a system has to be implemented in a realistic environment with multiple users. Who to ask a feedback when the system is uncertain about a collaborative activity? In this paper, we investigate this and more questions on this topic, proposing a preliminary study of the requirements of an active learning interface for multi-inhabitant settings. In particular, we formalize the problem and we describe the solutions adopted in our system prototype.
Selenium based Testing Systems for Analytical Data Generation of Website User Behavior

In recent years, websites have automatically sorted out content suitable for individual users on the website by learning user behavior patterns using AI. Website developers develop a function to generate behavioral data for analysis. The behavior data for analysis records the operation history of all functions such as content expression and button press. However, many consumer websites have a problem of enormous amount of testing due to continuous and frequent development. In this study, we propose a method for efficiently performing browser tests using Selenium by managing the specifications of the behavior data generation function.
Ranking of Countries using R

The growth of each and every country in the world leads to World Development which can be in terms of imports, exports, GDP, production, population, etc. The various factors play a very important role in analyzing a country's growth globally. There are tools to rank multiple countries based on a single indicator but there is no tool available for comparing and predicting the future trends on the basis of various indicators. Also, it is difficult for common people to find the data and then to compare on these indicators. So, we deduce a tool using data mining techniques to find the ranking of each country based on given parameters. This tool will be a boon for the companies, the officials and the common people.
Work-in-Progress—The SORBET Project: Designing an Immersive Environment Which Models the Diffusion of Virus Infection to Help Students of Mathematics Learn Probabilities

This work-in-progress paper describes preliminary and on-going work in the design and use of an open-source immersive environment (OpenSim) to help mathematics students in secondary schools in Singapore learn about, tinker with, and discuss probabilistically-driven scenarios of the diffusion of virus infection. The environment was designed and developed in April 2020 and is being piloted in three schools in May 2020, with a view to possible scaling in the second half of the year.
EDUCATIONAL ACTIVITY PROPOSAL FOR THE DEVELOPMENT OF EMPATHY IN BEGINNER STUDENTS OF COMPUTER SCIENCE

A. Aguelo1, C. Sanz2, T. Coma1, S. Baldassarri1, P. Álvarez1 1Universidad de Zaragoza (SPAIN) 2Universidad Nacional de La Plata (ARGENTINA)
Enhanced Data Processing System for Real Time Video Monitoring

Real time videos have become a part of all security and monitoring systems that are used in day to day life, processing information from a video without a human intervention is a challenging task. Video processing techniques have been widely adapted to process real time video but the challenge lies in the efficiency of processing the information and storing it. The research involved here coins a novel approach of using video processing techniques along with Big Data processing technologies which process a video at much faster rate and stores it efficiently for further references. The paper presents a comparison of Hadoop based Image processing package (HIPI) with Map Reduce and normal video processing using YOLO algorithm in terms of number of images, processing time and storage. The results indicate that the video processing system is enhanced by integrating with Hadoop based storage structure and storing the frames with MapReduce functionalities.
Toward Public Opinion Monitoring System of Large-Scale Data with Lambda Architecture

The public opinion monitoring system can monitor the latest information at any time and manage the network information activities by focusing on the website information about hot issues and key areas. The traditional Lambda architecture does not write abstract data on the storage layer, and the data latency and cost are relatively high on the storage. In this paper, we use the improved Lambda-based architecture to support stream and batch computing to make computational logic reused. The improved Lambda architecture enables the storage of unified historical data in full volume and online real-time incremental data. The storage can support high-throughput real-time write, as well as multi-field combination search and full-text retrieval.
Multiple-Differential Mechanism for Collision-Optimized Divide-and-Conquer Attacks

Several combined attacks have shown promising results in recovering cryptographic keys by introducing collision information into divide-and-conquer attacks to transform a part of the best key candidates within given thresholds into a much smaller collision space. However, these Collision-Optimized Divide-and-Conquer Attacks (CODCAs) uniformly demarcate the thresholds for all sub-keys, which is unreasonable. Moreover, the inadequate exploitation of collision information and backward fault tolerance mechanisms of CODCAs also lead to low attack efficiency. Finally, existing CODCAs mainly focus on improving collision detection algorithms but lack theoretical basis. We exploit Correlation-Enhanced Collision Attack (CECA) to optimize Template Attack (TA). To overcome the above-mentioned problems, we first introduce guessing theory into TA to enable the quick estimation of success probability and the corresponding complexity of key recovery. Next, a novel Multiple-Differential mechanism for CODCAs (MD-CODCA) is proposed. The first two differential mechanisms construct collision chains satisfying the given number of collisions from several sub-keys with the fewest candidates under a fixed probability provided by guessing theory, then exploit them to vote for the remaining sub-keys. This guarantees that the number of remaining chains is minimal, and makes MD-CODCA suitable for very high thresholds. Our third differential mechanism simply divides the key into several large non-overlapping “blocks” to further exploit intra-block collisions from the remaining candidates and properly ignore the inter-block collisions, thus facilitating the later key enumeration. The experimental results show that MD-CODCA significantly reduces the candidate space and lowers the complexity of collision detection, without considerably reducing the success probability of attacks.
Distributed Dispatching in the Parallel Server Model

With the rapid increase in the size and volume of cloud services and data centers, architectures with multiple job dispatchers are quickly becoming the norm. Load balancing is a key element of such systems. Nevertheless, current solutions to load balancing in such systems admit a paradoxical behavior in which more accurate information regarding server queue lengths degrades performance due to herding and detrimental incast effects. Indeed, both in theory and in practice, there is a common doubt regarding the value of information in the context of multi-dispatcher load balancing. As a result, both researchers and system designers resort to more straightforward solutions, such as the power-of-two-choices to avoid worst-case scenarios, potentially sacrificing overall resource utilization and system performance. A principal focus of our investigation concerns the value of information about queue lengths in the multi-dispatcher setting. We argue that, at its core, load balancing with multiple dispatchers is a distributed computing task. In that light, we propose a new job dispatching approach, called Tidal Water Filling, which addresses the distributed nature of the system. Specifically, by incorporating the existence of other dispatchers into the decision-making process, our protocols outperform previous solutions in many scenarios. In particular, when the dispatchers have complete and accurate information regarding the server queues, our policies significantly outperform all existing solutions.
Multiagent live programming systems: models and prospects for critical applications

Live programming constitutes a human-computer symbiosis in which a human creative activity and a continuous computer execution influence each other. Usually, there is a medium of expression called "code" that the human(s) use to express desired behavior on the part of the computer, and the computer provides its feedback in the form of textual, graphical, audio, or other output. The most popular domain for live programming has been music synthesis ("live coding"), but the key features of live programming suggest it can play an important role in other applications, even process control or emergency management.
An Introduction to PAKDD CUP 2020 Dataset

With the rapid development of cloud services, disk storage has played an important role in large-scale production cloud systems. Predicting imminent disk failures is critical for maintaining data reliability. Our vision is that it is important for researchers to contribute to the development of new techniques for accurate and robust disk failure prediction. If researchers can discover any reasonable approaches for disk failure prediction in large-scale cloud systems, all IT and big data companies can benefit from such approaches to further enhance the robustness of the production cloud systems. With this vision in mind, we have published an open labeled dataset that spans a period of 18 months with a total of 220,000 hard drives collected from Alibaba Cloud. Our dataset is among the largest released in the community in terms of its scale and duration. To better understand our dataset, we present our dataset generation process and conduct a preliminary analysis on the characteristics of our dataset. Our open dataset has been adopted in the PAKDD2020 Alibaba AI Ops Competition, in which contestants proposed new disk failure prediction algorithms through the analysis and evaluation of the dataset.
Efficient Mapping Method for Elliptic Curve Cryptosystems Based on PWLCM

The security of digital images is an important issue that has been receiving considerable attention in the recent past. Different image encryption techniques have been proposed in the literature. Among the fundamental theories in the number theory, we can find the elliptic curve (EC) which is widely used to construct cryptographic primitives. This paper investigates the security of image encryption schemes based on elliptic curves cryptography (ECC) and Chaos theory. More precisely, in this paper, we propose a new image encryption scheme that utilize a new mapping method based on Piecewise Linear Chaotic Map (PWLCM) that converts each pixel of plain image into a point on an elliptic curve. Encryption and decryption process are given in details. After applying encryption, security analysis is performed to show that our scheme cannot only achieve good encryption, but also resist the statistical attacks.