Reviewerly/exHarmony huggingface dataset:
Area-Time Scalable High Radix Montgomery Modular Multiplier for Large Modulus

Adoption of large modulus and field sizes for RSA and ECC are of the norm for meeting present day’s security requirements and goals. Modular multiplication (MM) is the key computational unit for both and requires large hardware resources (area) and incurs huge latency. Typically, Montgomery Modular Multiplier and Interleaved Modular Multiplier are being adopted for RSA and ECC respectively for designing efficient hardware. Lower Area-time (AT) product is the key design metric to consider both latency (time to compute one MM) and area together as a combined metric. Combinational circuit to compute partial products of a particular modulus for higher radices increases area and critical path, reduces frequency, and latency. Here, the increase in area dominates the reduction in latency with respect to radix, resulting in an increased AT metric for the given modulus. This work presents a table look up technique for the multiplication factors which is used to compute partial products of modulus in MR-MMM (Multi-Radix Montgomery Modular Multiplier). This approach reduces the AT metrics with increasing radices for a given larger modulus enabling an AT scalable hardware and has been proven on Virtex 7 and 6 FPGAs for modulus 256 to 4096 with radices 2 to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> .
Privacy-Preserving Proof-of-Location With Security Against Geo-Tampering

A Proof-of-Location (POL) system is used to issue a proof-of-location token ( <inline-formula><tex-math notation="LaTeX">$pol$</tex-math></inline-formula> ) to a user who has been present at a location <inline-formula><tex-math notation="LaTeX">$\ell oc$</tex-math></inline-formula> , such that it can be later presented to a verifier to assure the presence of the user at <inline-formula><tex-math notation="LaTeX">$\ell oc$</tex-math></inline-formula> . Basic POL security requirements are <i>unforgeability</i> of <inline-formula><tex-math notation="LaTeX">$pol$</tex-math></inline-formula> , and its <i>non-transferability</i> (a <inline-formula><tex-math notation="LaTeX">$pol$</tex-math></inline-formula> issued to user <inline-formula><tex-math notation="LaTeX">$u_1$</tex-math></inline-formula> cannot be used by <inline-formula><tex-math notation="LaTeX">$u_2$</tex-math></inline-formula> ). An additional important property of POL systems is <i>user privacy</i> against the issuers and verifiers. We make two contributions. First, we formalize the POL security and privacy properties, and construct the first system providing provable security and privacy against the issuer and the verifier, both. Second, we introduce a <i>geo-tampering attack</i> that completely breaks POL system security, by simply changing the location of a <inline-formula><tex-math notation="LaTeX">$pol$</tex-math></inline-formula> issuing node. The attack applies to portable infrastructure nodes that are not continually monitored. We propose an algorithm that is used by a <inline-formula><tex-math notation="LaTeX">$pol$</tex-math></inline-formula> issuer to provide a location integrity “proof”, that will be embedded in a <inline-formula><tex-math notation="LaTeX">$pol$</tex-math></inline-formula> to protect against this attack. The proof relies on a novel application of euclidean Distance Matrices. We implemented our POL on an off-the-shelf Android smartphone to show the practicality of the proposed algorithms.
Not All Noise is Accounted Equally: How Differentially Private Learning Benefits from Large Sampling Rates

Learning often involves sensitive data and as such, privacy preserving extensions to Stochastic Gradient Descent (SGD) and other machine learning algorithms have been developed using the definitions of Differential Privacy (DP). In differentially private SGD, the gradients computed at each training iteration are subject to two different types of noise. Firstly, inherent sampling noise arising from the use of minibatches. Secondly, additive Gaussian noise from the underlying mechanisms that introduce privacy. In this study, we show that these two types of noise are equivalent in their effect on the utility of private neural networks, however they are not accounted for equally in the privacy budget. Given this observation, we propose a training paradigm that shifts the proportions of noise towards less inherent and more additive noise, such that more of the overall noise can be accounted for in the privacy budget. With this paradigm, we are able to improve on the state-of-the-art in the privacy/utility tradeoff of private end-to-end CNNs.
Onboarding and Training New Electronic Resources Personnel in Libraries: An Overview of Literature and Resources

Onboarding is the process in which new employees are introduced to an organization and its vision, mission, and values. Library literature reflects the importance of onboarding in welcoming new employees and positioning them for success in their roles, but many established onboarding programs and practices have been interrupted by the COVID-19 pandemic and shifts to online and remote work. This column provides an overview of library literature and resources for onboarding and supporting new employees in the area of electronic resources, with a focus on resources and processes that are available online.
Using ICT to Teach Effectively at COVID-19

Biology pre-service teachers in 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> and 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> university year should perform 14-days pedagogic practice in lower secondary or secondary school. Because of the COVID-19 outbreak, the pedagogical practice was conducted as distance online education. Overnight, they were expected to adopt online environments. How successfully the pre-service teachers implemented the pedagogical practice of the university program Subject Teacher, Biology, Faculty of Science and Mathematics, University of Maribor, during COVID-19 is the content of this paper. The paper presents a SWOT analysis of the implementation of pedagogical practice in lower secondary and secondary schools in distance online education, during COVID-19. The key competences developed in distance education, the experience gained, the usefulness of ICT, which ICT is useful in distance education and the personal feelings of pre-service teachers before and after the practice are highlighted. Most pre-service biology teachers describe experience as good, exceptional and something quite different from the original expectations. Biology pre-service teachers are grateful for new knowledge about computer applications that are very useful in biology distance education (Powtoon, Filmora, Audacity, Quizlet, Zygote body, Moodle, Padlet, etc.). The main disadvantage of distance learning is the lack of direct contact with students and the time delay, as they spend 3 to 5 times more time preparing materials for distance learning than for performances in school settings.
ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS

Artificial Intelligence (AI) and Deep Learning (DL) algorithms are currently applied to a wide range of products and solutions.DL training jobs are highly resource demanding and they experience great benefits when exploiting AI accelerators (e.g., GPUs).However, the effective management of GPU-powered clusters comes with great challenges.Among these, efficient scheduling and resource allocation solutions are crucial to maximize performance and minimize Data Centers operational costs.In this paper we propose ANDREAS, an advanced scheduling solution that tackles these problems jointly, aiming at optimizing DL training runtime workloads and their energy consumption in accelerated clusters.Experiments based on simulation demostrate that we can achieve a cost reduction between 30 and 62% on average with respect to first-principle methods while the validation on a real cluster shows a worst case deviation below 13% between actual and predicted costs, proving the effectiveness of ANDREAS solution in practical scenarios.
VibeRing: Using vibrations from a smart ring as an out-of-band channel for sharing secret keys

Many Internet of Things (IoT) devices are capable of sensing their environment, communicating with other devices, and actuating on their environment. Some of these IoT devices, herein known as “smartThings”, collect meaningful information from raw data when they are in use and in physical contact with their user (e.g., a blood-glucose monitor); the smartThing’s wireless connectivity allows it to transfer that data to its user’s trusted device, such as a smartphone. However, an adversary could impersonate the user and bootstrap a communication channel with the smartThing while the smartThing is being used by an oblivious legitimate user. To address this problem, in this paper, we investigate the use of vibration, generated by a smartRing, as an out-of-band communication channel to unobtrusively share a secret with a smartThing. This exchanged secret can be used to bootstrap a secure wireless channel over which the smartphone (or another trusted device) and the smartThing can communicate. We present the design, implementation, and evaluation of this system, which we call VibeRing. We describe the hardware and software details of the smartThing and smartRing. Through a user study we demonstrate that it is possible to share a secret with various objects quickly, accurately and securely as compared to several existing techniques. Overall, we successfully exchange a secret between a smartRing and various smartThings, at least 85.9% of the time. We show that VibeRing can perform this exchange at 12.5 bits/second at a bit error rate of less than 2.5%. We also show that VibeRing is robust to the smartThing’s constituent material as well as the holding style. Finally, we demonstrate that a nearby adversary cannot decode or modify the message exchanged between the trusted devices.
Deep Contextualized Compressive Offloading for Images

Recent years have witnessed sensors becoming an indispensable part of our life with the camera being one of the most popular and widely deployed sensors. The camera gives rise to numerous vision-based IoT applications that generate high-level understandings of a live video stream by performing analysis on end devices like mobile or embedded devices. Typically, these applications are built with deep learning (DL) models to conduct complex vision tasks, e.g., image classification and object detection. Due to the prohibitive cost of running DL models on end devices close to the camera and with limited computation capabilities, it is widely adopted to offload the computation to a nearby powerful edge server. However, there is a gap between the restricted offloading bandwidth of the end device and the large volume of image data incurred by the live video stream. In this paper, we present Deep Contextualized Compressive Offloading for Images (DCCOI), a lightweight, context-aware, and bandwidth-efficient offloading framework for images. DCCOI consists of the spatial-adaptive encoder, a lightweight neural network, to spatial-adaptively compress the image, and the generative decoder for reconstructing the image from the compressed data. In contrast to existing DL-based encoders, the spatial-adaptive encoder allows an image region to be encoded into different numbers of feature values based on the information in it. This offers a variable-length coding method for image compression, which is a more optimal way for compression than the fix-length coding method took by existing DL-based compression approaches and demonstrates superior accuracy-compression rate trade-offs. We evaluate DCCOI against several baseline compression techniques while serving an object detection-based application. The results show that DCCOI roughly reduces the offloading size of JPEG by a factor of 9 and DeepCOD, the state-of-the-art offloading approach, by 20% with similar accuracy and a compression overhead less than 50ms.
Fine-Grained Emotion Prediction by Modeling Emotion Definitions

In this paper, we propose a new framework for fine-grained emotion prediction in the text through emotion definition modeling. Our approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. Our models outperform existing state-of-the-art for fine-grained emotion dataset GoEmotions. We further show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the generalization capability of the models.
On the dissipative dynamics of entangled states in coupled-cavity quantum electrodynamics arrays

We examine the dissipative dynamics of N00N states with an arbitrary photon number N in two architectures of fiber-coupled optical ring resonators (RRs) interacting with two-level quantum emitters. One architecture consists of a two-way cascaded array of emitter-cavity systems, while in the other architecture we consider two fiber-coupled RRs each coupled to multiple dipole-dipole interacting (DDI) quantum emitters (QEs). Our focus in this paper is to study how am initially prepared multiple excitation atomic N00N states transfers to the RRs and then how rapidly it decays in these open cavity quantum electrodynamics (CQED) setups while varying the emitter-cavity coupling strengths, emitter-cavity detuning, and backscattering from cavity modes. We present a general theoretical formalism valid for any arbitrary numbers of QEs, RRs, and N number in the N00N state for both schemes. As examples, we discuss the cases of single and two-excitation N00N states and report the comparison of our findings in both schemes. As one of the main results, we conclude that the array scheme tends to store N00N for longer times while the DDI scheme supports higher fidelity values. The results of this study may find applications in designing new multiparty entanglement-based protocols in quantum metrology and quantum lithography.
Sentiment Analysis of Twitter Data Using Naïve Bayes Classifier

Nowadays social networking is creating a rush of users’ data. Since Microblogging sites like Facebook, Twitter, etc. provides an easyand short way to express and share their view of millions of users daily. With increase of use of the microblogs people express their opinions on variety of topics, companies, products etc. These helps a lot for the company to propagate their business by analyzing people’s opinion. The intention of this paper is to develop a model that analyzes the sentiment behind the tweets gathered from the Twitter. The challenge is the data that are there in Twitter are extremely unformed which makes difficult to study. In our model, the sentiment analysis process is as follows: (1) Collect tweets directly using Twitter API, (2) Data Cleaning, (3) Training the data into our model i.e. Naïve Bayes, (4) Extracted tweets are classified by its sentiment i.e. its polarity (positive, negative or neutral). In our work we have used a supervised machine learning algorithm i.e. Naïve Bayes Classifier.
Robot Path Planning Using Q- Learning Algorithm

In order to reduce the loss of resources and improve the efficiency of the system, efficient methods are required to solve mobile robot maze. In this paper, the Q-learning method is applied to enable the constantly update of the Q table for robot under the feedback of the maze. Specifically, it is essentially an iterative process, which adjusts the path during moving and uses the optimal path to find the correct exit eventually. By comparing the way in different environment we draw a conclusion that through Q-learning the robot can find the shortest way to solve the maze.
Integrating ADTs in KeY and Their Application to History-Based Reasoning

We discuss integrating abstract data types (ADTs) in the KeY theorem prover by a new approach to model data types using Isabelle/HOL as an interactive back-end, and translate Isabelle theorems to user-defined taclets in KeY. As a case study of this new approach, we reason about Java’s Collection interface using histories, and we prove the correctness of several clients that operate on multiple objects, thereby significantly improving the state-of-the-art of history-based reasoning.Open Science. Includes video material [] and a source code artifact [].
ShieldDB: An Encrypted Document Database With Padding Countermeasures

Cloud storage systems have seen a growing number of clients due to the fact that more and more businesses and governments are shifting away from in-house data servers and seeking cost-effective and ease-of-access solutions. However, the security of cloud storage is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB’s padding strategies ensure that the access pattern to the database is obfuscated all the time. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud.
Design of an Automated Multiposition Dynamic Wheelchair

This work presents a design for an automatized multiposition dynamic wheelchair used to transport quadriplegic patients by reconfiguring a manual wheelchair structure. An electric actuator is attached to a four-bar mechanism fixed to each side of a wheelchair's backrest to reach multiposition. The entire device is actuated through a PID controller. An experimental test is carried out in a simplified wheelchair structure. Finally, the structure of the wheelchair is evaluated through the Dynamic analysis and Finite Element Method under the payload computed with the most critical position reached by the mechanism.
HybridPointing for Touch

We propose CursorTap, an extension of Forlines et al.'s mixed, absolute and relative "HybridPointing" to large wall-sized multitouch displays. Our technique uses a relative pointing quasimode activated with one hand, while the other hand controls a distant cursor similar to a large touchpad. A controlled experiment compares the technique to standard absolute touch input as a baseline and a whole-display "Drag" technique representing a common alternate approach. Results show CursorTap is fastest for the common usage scenario of reaching distant targets and then returning to nearby targets. Overall, median selection times across distances are similar with CursorTap, but linearly increase with the other techniques. As further validation, a second study explore show people use CursorTap in a two-person game. The results found just over half of the participants choose to use CursorTap for half of the primary interactions where "enemies" are eliminated using a tap, drag, or lasso "tool".
VIP: Safeguard Value Invariant Property for Thwarting Critical Memory Corruption Attacks

Most modern software attacks are rooted in memory corruption vulnerabilities, which are capable of altering security-sensitive data (e.g., function pointers) to unintended values. This paper introduces a new security property, the Value Invariant Property (VIP), and HyperSpace, our prototype that enforces VIP on security-sensitive data. HyperSpace safeguards the integrity of "data values" instead of enforcing control/data flow, allowing for low runtime overhead, yet defeating critical attacks effectively. We implement four representative security policies including Control Flow Integrity (VIP-CFI), Code Pointer Integrity (VIP-CPI), Virtual function Table protection (VIP-VTPtr), and heap metadata protection based on HyperSpace. We evaluate HyperSpace with SPEC CPU2006 benchmarks and real-world applications (NGINX and PostgreSQL) and test how HyperSpace defeats memory corruption-based attacks, including three real-world exploits and six attacks that bypass existing defenses (COOP, heap exploits, etc.). Our experimental evaluation shows that HyperSpace successfully stops all these attacks with low runtime overhead: 0.88% and 6.18% average performance overhead for VIP-CFI and VIP-CPI, respectively, and overall approximately 13.18% memory overhead with VIP-CPI in SPEC CPU2006.
Engagement and time perception in virtual reality

Virtual reality (VR) is a simulation tool that is being used extensively to study the effects of training and perception. However, several studies have shown that some aspects of perception within VR are not always accurate. The present study investigates the perception of time within a VR environment by asking for retrospective time judgments of the length of VR experiences. These environments varied in both the level of interaction with the VR environment, and also the spatial properties of the environment itself. The judged length of time did not significantly differ between conditions based on the level of activity in the environment. However, the spatial properties of the VR environment did produce significantly different time estimations. This finding suggests that careful attention should be paid to what and how users are trained or evaluated in VR.
3D Photography with One-shot Portrait Relighting

3D photography is a fascinating way to synthesize novel views from limited captured views by using image-based rendering techniques. However, due to the lack of consideration on the light condition, the existing methods cannot achieve vivid results for augmented reality systems and virtual reality systems. In this paper, we present a physical-based framework that explicitly models 3D photography with relighting from a one-shot portrait. Instead of directly rendering new views, we first propose a facial albedo extraction network (FAE-Net) for synthesizing new views under different light conditions. In order to render more realistic reflected light, we come up with a solution for accurate mesh reconstruction through fine-grained portrait depth estimation. By taking advantage of these two technical components, our method is capable of generating novel views with different lighting conditions, which can faithfully deliver realistic rendered results. Extensive experiments show the proposed method can achieve better visual results.