Miracle-dz/UniFAR-FTUs huggingface dataset:
TROD: Throughput-Optimal Dynamic Data Traffic Management in Software-Defined Networks

In this paper, we study the problem of dynamic data traffic management in software-defined networks (SDNs) in the presence of Internet-of-things (IoT) devices, where IoT devices act as the data traffic sources. In the existing literature, researchers focused on efficient utilization of the ternary content-addressable memory (TCAM) in data traffic management. However, in the presence of IoT-devices, the unbalanced data traffic in SDN deteriorates the overall network performance. Hence, there is a need to design a throughput-optimal dynamic data traffic management scheme for SDN in the presence of IoT-devices, while minimizing the network delay. In this work, we propose an extensive game theory-based scheme, named TROD, for dynamic data traffic management. Additionally, we prove that the dynamic data traffic management, while minimizing the delay and maximizing the throughput of SDN in the presence of IoT-devices, is an NP-complete problem. Hence, we use an evolutionary game theoretic approach to obtain a sub-optimal problem. Thereafter, using linear programming, we obtain the optimal time division matrix, which ensures optimal throughput and delay in SDN. Through simulation, we observe that using TROD, proper distribution of data traffic among the available switches is ensured, and the volumetric overhead per switch reduces by 23.4-29.7%.
Enriching IAPS and GAPED Image Datasets with Unrestrained Emotional Data

Elicitation of emotions is typically done through the presentation of emotionally salient material, like images or videos, thus requiring reliably annotated datasets. Although there are datasets with emotional information, these only describe either emotional polarities or discrete emotions. The only available dataset with both types of information restrained the participants during the study by separating a priori the images according to their polarity (positive or negative). In this paper, we describe an unrestrained study with 60 participants, where we asked them to rate the polarities and discrete emotions elicited by a set of images. The analysis of the emotional ratings made by the users revealed the most frequent correlations between the basic emotions. Furthermore, the analysis of the ratings’ agreement among participants and existing datasets shows that our results are aligned with the existing ones. As a result of our study, we make available to researchers a more informative picture dataset annotated with emotional polarities and multiple emotions, as a complement to existing datasets.
Sum-Rate Maximization Under QoS Constraints for WPCN with User Cooperation

In this paper, we consider a two-user wireless powered communication network (WPCN) with user cooperation, which consists of a hybrid access point (H-AP) and two users with energy harvesting circuit. The H-AP transfers energy to users during downlink (DL) and the users transmit information during uplink (UL) utilizing the harvested energy. To enhance user fairness, a user cooperation protocol is employed in which a user near from the H-AP relays the message of the other user by partially using its allocated time and energy resources. We maximize the sum-rate in the considered network subject to a set of quality of service (QoS) requirements by jointly optimizing the time allocation for DL energy transfer and UL information transmission. A novel closed-form solution is derived for the optimization problem using convex optimization techniques.
Efficient Outsourced Data Access Control with User Revocation for Cloud-Based IoT

Data owners have benefited significantly from cloud computing for managing the numerous data produced by massive devices in various Internet of Things (IoT) applications, such as smart home and electronic healthcare. On the other hand, fine- grained access control on outsourced data is a big concern for data owners, after they lose physical control over their data. Key-policy attribute- based encryption (KP-ABE), which provides data confidentiality and fine-grained data access control simultaneously, can be naturally introduced in this cloud-based IoT paradigm. However, the primitive KP-ABE cannot achieve efficient data access control with flexible user revocation. In this paper, we propose an efficient and fine-grained data access control scheme based on the proxy re-encryption and key blinding techniques for cloud-based IoT. With the scheme, the decryption capability of misbehaving users can be efficiently revoked to prevent data disclosure. In addition, most of the costly update operations over ciphertexts and keys due to user revocation, are delegated to the cloud. Extensive experiment results demonstrate that our scheme is more efficient than existing solutions in terms of computation and communication overheads.
An Overview on the Convergence of High Performance Computing and Big Data Processing

At present, with the rapid development of big data processing technology, streaming data processing and real-time data analysis have gradually become new research hotspots. Both the industry and the academia have invested a lot of research into the efficient processing methods of massive data generated in the environment such as the Internet and e-commerce. Meanwhile, high-performance computing technology and supercomputers are also looking for new business growth points. The convergence of big data processing and high performance computing technology is the general trend of big data analysis in the future. This paper will give a brief overview of typical technologies in the fusion process of big data processing and high-performance computing.
Multi-Level Energy Consumption Optimization for Smart Buildings

One of the most interesting challenges in the modern grids is the consumer demand management and optimization. However, the Information and Communication Technologies (ICTs) can offer promising solutions for this challenge. In this paper, we introduce a distributed multi-level solution for the energy consumption optimization. The proposed solution offers an automated demand control mechanism, using a Wireless Sensor Actuator Network (WSAN), to reduce the energy consumption of each consumer. Moreover, we propose a scheduling scheme that minimizes the total cost of a building energy demand based on a cooperative game model to achieve the collective goal. We introduce also a mechanism that improves the consumer satisfaction when the available energy is insufficient. The performance evaluation shows that the proposed approach reduces the energy demand, the total consumption cost, and the peak to average ratio.
Antipodal Detection and Decoding for Large Multi-User MIMO with Reduced Base-Station Antennas

To avoid unnecessarily using a massive number of base station antennas for supporting a large number of users in spatially multiplexed multi-user MIMO systems, optimal detection methods are required to demultiplex the mutually interfering information streams. Sphere decoding (SD) can achieve this, but its complexity and latency becomes impractical for large MIMO systems. Low complexity detection solutions such as linear detectors (e.g., MMSE) or likelihood ascendant search (LAS) approaches, have significantly lower latency requirements than SD but their achievable throughput is far from optimal. This work presents the concept of Antipodal detection and decoding, that can deliver very high throughput with practical latency requirements, even in systems where the number of user antennas reaches the number of base station antennas. The Antipodal detector either results in a highly reliable vector solution, or it does not find a vector solution at all (i.e., it results in an erasure), skipping the heavy processing load related to finding vector solutions that have a very high likelihood to be erroneous. Then, a belief-propagation-based decoder is proposed, that restores these erasures and further corrects remaining erroneous vector solutions. We show that for $32\times 32$, 64-QAM modulated systems, and for packet error rates below 10%, Antipodal detection and decoding requires 9 dB less transmitted power than systems employing soft MMSE or LAS detection and LDPC decoding with similar complexity requirements. For the same scenario, our Antipodal method achieves practical throughput gains of more than 50% compared to soft MMSE and soft LAS-based methods.
A Fast Vehicular Traffic Flow Prediction Scheme Based on Fourier and Wavelet Analysis

Currently, traffic congestion has become a part of daily life of people in the cities around the world, and impacts people's lives adversely, e.g., the extra time spent on commuting, the extra exhaust emissions, etc. In order to reduce the effects of congestion on our lives, intensive research efforts have been proposed on this issue. Intelligent Transportation System (ITS) is one of potential solutions to enable various applications to improve road safety and travel comfort, and has gained a lot of attention from researchers around the world. In order to efficiently manage the transportation system and reduce traffic congestion, one of the paramount problems needed to be solved in ITS is the accurate traffic prediction. In this article, we firstly combine Fourier analysis with wavelet denoising technique to cope with the traffic flow forecasting problem. A two-layer fast Fourier transform (FFT)-based traffic prediction scenario is proposed, in which the discrete wavelet transform (DWT) with two different threshold values are adopted to decompose the high-frequent-noise and identify low-frequent traffic flow changing trend from the original data. Three different data sets with different traffic flow patterns are chosen from the England Highways data set to test our proposed work. Intensive simulations are implemented to verify the proposed work.
Reliable ReRAM-based Logic Operations for Computing in Memory

The development of non-conventional Von-Neumann architectures becomes essential for breakthrough computing in Internet of Things (IoT) devices. The main objective for IoT application is to lower as much as possible the power consumption to promote autonomy. The key to solve this challenge is to reduce the data transfer between memory and computing unit. As emerging non-volatile memories and especially resistive switching technologies (ReRAM) can today be co-integrated with CMOS on hybrid process, we propose in this paper to develop bitwise logic operations inside and close to the memory array. Using two transistors – one ReRAM (2T1R) memory cell architecture with differential approach to enhanced read reliability, we can perform logic operations without impacting the global memory architecture. Thanks to parallel data sensing, the structure enables fast computation of any bitwise logic operations (ID, AND, OR, XOR in their natural or complementary form) with high reliability, promoting the computing in memory (CiM) concept.
Stride in BlueJ -- Computing for All in an Educational IDE

Block-based programming languages and environments have several benefits for introductory programming courses, compared to more traditional text-based languages. In particular, blocks remove the burden of learning language syntax and dealing with syntax-related errors. Many blocks-based environments are tightly focused on developing graphical games, stories and simulations, while the more general programming environments are typically text-based. In this tool paper, we describe the incorporation of a Stride editor within the BlueJ programming environment. Stride is a frame-based programming language, intended to combine the best of blocks and text programming, usable both as a stepping stone towards text-based languages and as a comprehensive language in its own right. The incorporation of Stride into BlueJ brings some aspects of block programming into a general purpose educational environment.
Behavioural Compliance and Law Enforcement in Online Hate Speech

It is usually said that technical solutions should operate ethically, in compliance with the law and subject to good governance principles. In this position paper we face the problem of behavioural compliance and law enforcement in the case of hate and fear speech online. Law enforcement and behavioural compliance are ways of coping with the objective of stopping hate online. We contend that a combination of regulatory instruments, incentives, training, proactive selfawareness and education can be effective to create legal ecosystems to improve the present situation.
AI for K-12: Making Room for AI in K-12 CS Curricula

As CS expands into more K-12 classrooms and children become familiar with computational thinking, advances in AI pose new challenges for CS educators. Children now enjoy conversing with AI-powered agents such as Alexa and Siri, while their parents worry about the imminent arrival of autonomous robots and self-driving cars. As AI technologies become more prominent in our lives, we need to consider what every child should know about AI. This BOF provides a timely opportunity to introduce CS educators and researchers to several AI for K-12 efforts, including available curricula, tools, and resources. Attendees will discuss how AI can best be incorporated into the K-12 CS curriculum, the tools/resources that will be needed to support students and teachers learning about AI, and how AI education might impact their own work. This BOF is complementary to the SIGCSE 2019 Special Session: AI for K-12 Guidelines Initiative that introduces the current draft of our 'Big Ideas in AI. " Further information about the initiative and resources is available at http://ai4k12.org.
Evaluating the Impact of Process Variability and Radiation Effects on Different Transistor Arrangements

The high integration capacity of digital circuits, which occurs due to technological scaling, presents new challenges for nanotechnology designs. The evolution of integrated circuits has made them more susceptible to faults, besides increasing the process variability, which can lead to circuits operating outside their specification ranges. This work evaluates the effects of process variability and radiation faults on complex gates. These effects are compared to alternative circuits that implement the same functions but exploring a multi-level of basic cells as NAND2, NOR2 and Inverters. The technology adopted is 7nm FinFET ASAP. Results show that although complex cells present better timing and power results, multi-level circuits are up to 28% less sensible to radiation faults and about 40% more stable under process variability.
Performance Prediction of GPU-Based Deep Learning Applications

Recent years saw an increasing success in the application of deep learning methods across various domains and for tackling different problems, ranging from image recognition and classification to text processing and speech recognition. In this paper we propose and validate an approach to model the execution time for training convolutional neural networks (CNNs) deployed on GPGPUs. We demonstrate that our approach is generally applicable to a variety of CNN models and different types of G PG PU s with high accuracy, aiming at the preliminary design phases for system sizing.
Making Mii: studying the effects of methodological approaches and gaming contexts on avatar customization

ABSTRACT In this paper we present an analysis of the character creation interface on the Nintendo WiiU and explore the pragmatics of avatar customisation through a between-subjects, qualitative user study involving 24 participants. Our Mii research – or ‘Miisearch’ – is motivated by our desire to highlight the agency these interfaces have in self-representational practices; specifically, to understand the effects of interface affordances on avatar customisation in games. Furthermore, as self-representational practices are often studied using distance methods and self-reporting (e.g. surveys), we compare data on avatar customisation from two sources: a survey and participant observation via micro-ethnography. Results of our study reveal discrepancies between the survey data and participant observation, challenging popular methodological approaches in both the game studies and HCI communities. Most significantly, our findings illustrate the combined effects of gaming contexts and interface affordances on avatar customisation.
An Examination of the Correlation of Exploring Computer Science Course Performance and the Development of Programming Expertise

This study investigated patterns in the development of computational thinking and programming expertise in the context of the Exploring Computer Science (ECS) program, a high school introductory CS course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. Prior research on programming expertise has identified three general areas of development --- program comprehension, program planning, and program generation. The pedagogical practices in ECS are consistent with problem solving approaches that support the development of programming expertise. The study took place in a large urban district during the 2016--17 school year with 28 ECS teachers and 1,931 students. A validated external assessment was used to measure the development of programming expertise. The results indicate that there were medium-sized, statistically significant increases from pretest to posttest, and there were no statistically significant differences by gender or race/ethnicity. After controlling for prior academic achievement, performance in the ECS course correlated with performance on the posttest. With respect to specific programming concepts, the results also provide evidence on the progression of the development of programming expertise. Students seem to develop comprehension and planning expertise prior to expertise in program generation. In addition, students seem to develop expertise with concrete tasks prior to abstract tasks.
An Efficient High-Rate Non-Binary LDPC Decoder Architecture With Early Termination

This paper presents a modified Trellis Min–Max (T-MM) algorithm together with the associated architecture for non-binary (NB) low-density parity-check (LDPC) decoders. The proposed T-MM algorithm is able to reduce the memory requirements for the check-node messages through an efficient compression method and enhance the error-rate performance using the appropriate decompression. A method of updating the <italic>a posteriori</italic> log-likelihood ratio in the delta domain is used to simplify the computational and storage complexity. In order to enhance the decoding throughput, a low-complexity early termination (ET) scheme is devised by using the hard decisions of the variable-to-check messages, where, although a minor overhead is introduced, there is no visible degradation in error rate. As a proof of concept, a row-parallel layered decoder for the 32-ary (837, 726) LDPC code is implemented using a 90-nm CMOS process. The proposed decoder achieves a throughput of 1.64 Gb/s at 526.32 MHz based on eight iterations and has an area of 6.86 mm<sup>2</sup>. When the ET scheme is enabled, the decoder achieves a maximum throughput of 4.68 Gb/s with a frame error rate of <inline-formula> <tex-math notation="LaTeX">$3.25\times 10^{-6}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$E_{b}/N_{0} = 4.5$ </tex-math></inline-formula> dB. The proposed NB-LDPC decoder achieves the highest throughput and hardware efficiency compared to the state-of-the-art decoders, even when the ET scheme is not enabled.
Code Process Metrics in University Programming Education

Code process metrics have been widely analyzed within large scale projects in the software industry. Since they reveal much about how programmers collaborate on tasks, they could also provide insights in the programming and software engineering education at universities. Thus, we investigate two courses taught at the University of Bamberg, Germany to gain insights into the success factors of student groups. However, a correlation analysis of eight metrics with the students’ scores revealed only weak correlations. In a detailed analysis, we examine the trends in the data per assignment and interpret this using our knowledge of code process metrics and the courses. We conclude that the analyzed programming projects were not suitable for code process metrics to manifest themselves because of their scope and students’ focus on the implementation of functionality rather than following good software engineering practices. Nevertheless, we can give practical advice on the interpretation of code process metrics of student projects and suggest analyzing projects of larger scope.
Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images

Histopathology images exhibit considerable variability, which can make diagnosis prone to uncertainty and errors. Using retrieval systems to locate similar images when a query image is given can assist pathologists in making more reliable decisions when diagnosing, based on accurately diagnosed past cases. Local binary patterns (LBP) have been successfully used as image descriptors for different applications. However, using LBP on histopathology images is still under investigation from different perspectives. The immense texture variability of these images and the lack of labeled training data are among the challenges that must be addressed. In this paper, we propose a new extension of LBP that is explicitly aware of the heterogeneity of local texture patterns through heterogeneity-based weighting. We use both homogeneity and the second moment (variance) of local neighborhoods to extend LBP histograms with heterogeneity information so that they better capture the polymorphism in histopathology images. We applied all concepts at multiple scales and investigated the extensions through both separated and concatenated extended histograms. We applied the proposed method on three publicly available datasets, namely, KimiaPath24, invasive ductal carcinoma (IDC), and BreakHis. The experimental results revealed that our features could retrieve and classify images with the highest accuracy. For KimiaPath24, we achieved 96.4% surpassing both LBP (91.0%) and deep features from VGG network (79.5%). For IDC, we reached the highest $F1$ measure (0.7665). Only for BreakHis, the proposed method was slightly less accurate than a customized convolutional neural network with rule fusion.