Reviewerly/exHarmony huggingface dataset:
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics

Differential privacy (DP) provides rigorous privacy guarantees on individual's data while also allowing for accurate statistics to be conducted on the overall, sensitive dataset. To design a private system, first private algorithms must be designed that can quantify the privacy loss of each outcome that is released. However, private algorithms that inject noise into the computation are not sufficient to ensure individuals' data is protected due to many noisy results ultimately concentrating to the true, non-privatized result. Hence there have been several works providing precise formulas for how the privacy loss accumulates over multiple interactions with private algorithms. However, these formulas either provide very general bounds on the privacy loss, at the cost of being overly pessimistic for certain types of private algorithms, or they can be too narrow in scope to apply to general privacy systems. In this work, we unify existing privacy loss composition bounds for special classes of differentially private (DP) algorithms along with general DP composition bounds. In particular, we provide strong privacy loss bounds when an analyst may select pure DP, bounded range (e.g. exponential mechanisms), or concentrated DP mechanisms in any order. We also provide optimal privacy loss bounds that apply when an analyst can select pure DP and bounded range mechanisms in a batch, i.e. non-adaptively. Further, when an analyst selects mechanisms within each class adaptively, we show a difference in privacy loss between different, predetermined orderings of pure DP and bounded range mechanisms. Lastly, we compare the composition bounds of Laplace and Gaussian mechanisms based on histogram datasets.
Radon–Sobolev Variational Auto-Encoders

The quality of generative models (such as Generative adversarial networks and Variational Auto-Encoders) depends heavily on the choice of a good probability distance. However some popular metrics like the Wasserstein or the Sliced Wasserstein distances, the Jensen–Shannon divergence, the Kullback–Leibler divergence, lack convenient properties such as (geodesic) convexity, fast evaluation and so on. To address these shortcomings, we introduce a class of distances that have built-in convexity. We investigate the relationship with some known paradigms (sliced distances – a synonym for Radon distances – reproducing kernel Hilbert spaces, energy distances). The distances are shown to possess fast implementations and are included in an adapted Variational Auto-Encoder termed Radon–Sobolev Variational Auto-Encoder (RS-VAE) which produces high quality results on standard generative datasets.
Collaborative Attention Network for Person Re-identification

Abstract The quality of visual feature representation has always been a key factor in many computer vision tasks. In the person re-identification (Re-ID) problem, combining global and local features to improve model performance is becoming a popular method, because previous works only used global features alone, which is very limited at extracting discriminative local patterns from the obtained representation. Some existing works try to collect local patterns explicitly slice the global feature into several local pieces in a handcrafted way. By adopting the slicing and duplication operation, models can achieve relatively higher accuracy but we argue that it still does not take full advantage of partial patterns because the rule and strategy local slices are defined. In this paper, we show that by firstly over-segmenting the global region by the proposed multi-branch structure, and then by learning to combine local features from neighbourhood regions using the proposed Collaborative Attention Network (CAN), the final feature representation for Re-ID can be further improved. The experiment results on several widely-used public datasets prove that our method outperforms many existing state-of-the-art methods.
Smartphone-based mobile assisted language learning application in higher vocational education in Indonesia

This study sought to examine the educational affordance and constraints of smartphone-based assistive technology in language learning from the students’ point of view along with their recommendations on the possible future advancement of smartphone-based assistive technology to help students engage better. This study employed content analysis in analyzing the data obtained from open-ended questions administered to students. The findings of this study denote the use of smartphones in widening vocabulary range, providing practices for English exercise, having attractive and interactive features, and fostering autonomy, self-regulation, and independence learning due to its practicality, portability, accessibility, and flexibility. This study highlights technical problems and confusion as the obstacles emerging from the use of smartphones in language learning. Meanwhile, the recommendations for future smartphone-based application updates are discussed in detail. All in all, smartphone-based assistive language learning application is fruitful for students and is recommended to be used for vocational higher education students as the part of informal learning during the COVID-19 pandemic situation. HIGHLIGHTS: Language learning has stepped forward to more personal learning tools by integrating smartphones as an aid for mobile learning. smartphones are fruitful in widening vocabulary range, providing practices for English exercise, having attractive and interactive features, and fostering autonomy, self-regulation, and independence learning due to its practicality, portability, accessibility, and flexibility. From language learning mobile application, students demanded simpler reading exercises and vocabularies and slower listening practice in the applications.
Understanding and deciphering of social engineering attack scenarios

Abstract Malicious scammers and social engineers are causing great harms to modern society, as they have led to the loss of data, information, money, and many more for individuals and companies. Knowledge about social engineering (SE) is wide‐spread and it exits in non‐academic papers and communication channels. Knowledge is mostly based on expert opinion and experience reports. Such knowledge, if articulated, can provide a valid source of knowledge and information. We performed the analysis of such sources, guided by academic principles around SE, and solicit existing SE scenarios from public awareness education materials, news stories, research literature, official advisories to public departments. We adopted grounded theory to extract the general knowledge behind SE, such as, attacking cycles, information gathering strategies, psychological principles, attack vectors, and so on. In this article, we aim to review and synthesize a body of knowledge (rationale and motivation of social engineers). The study aims to: (a) understand the rationale of social engineers; (b) capture the knowledge of SE attacks and extract important information from the sources; (c) propose an activity for counteracting SE attacks, and how it can be used in security education.
Blockchain based Joint Task Scheduling and Supply-Demand Configuration for Smart Manufacturing

Nowadays, blockchain has become a promising tamper-evident and tamper-resistant distributed ledger technology that achieves the security and privacy through the cryptography, consensus mechanism and chained data structure. In this paper, we propose a general blockchain-based smart manufacturing system (BSM) that utilizes the decentralization, immutability, auditability of blockchain to achieve flexible manufacturing that responds to on-demand services in time. For management, manufacturing services are divided into tasks and for unified scheduling, these tasks are queued along the logical flow in the transaction pool. Considering the contradiction between large scale manufacturing and limited transactional throughput, a joint task scheduling over blockchains and supply-demand configuration design is proposed to obtain the maximum customers' net profit while balancing the timeliness as well as the production and blockchain payoff. Moreover, a maximum weight matching based Alternating Optimization framework (MWMAO) is proposed as the solution. Simulation results show that the proposed framework has superiority on the profitability.
The Perception of Library and Information Science (LIS) Professionals about Research Data Management Services in University Libraries of Pakistan

Abstract Research data management services (RDMS) is considered as an emerging and groundbreaking area for research libraries. A large number of studies focused on researchers’ perspectives of how they perform research data management practices. There are some studies that examine this important area of research from library and information science (LIS) professionals’ context, especially developing countries like Pakistan. Hence, this study addresses the gap and investigate the RDMS training needs, motivational factors, possible hindrances, and key reasons to support RDMS. A survey method was used and a self-developed questionnaire was prepared using Google Docs survey. The questionnaire link was shared with LIS professionals considering purposive sampling technique. The study highlights the main RDMS supporting reasons, needed training areas, best methods to get training, the motivational factors, and possible hindrances while planning and implementing RDMS. This study fills the gap and addresses research data management literature in developing countries’ context, especially Pakistan, and established that RDMS are poorly observed in developing countries and require some drastic steps to be launched and improved. Higher Education Commission/departments, university administrations, and donor agencies take such initiatives that research data should be openly available through repositories and the maximum number of training opportunities should be provided to LIS professionals.
Memory Layers with Multi-Head Attention Mechanisms for Text-Dependent Speaker Verification

In this paper, we explore an approach based on memory layers and multi-head attention mechanisms to improve in an efficient way the performance of text-dependent speaker verification (SV) systems. The most extended SV systems based on Deep Neural Networks (DNN) extract the embedding of the utterance from the average pooling of the temporal dimension after processing. Unlike previous works, we can exploit the phonetic knowledge needed for text-dependent SV systems by combining the temporal attention of multiple parallel heads with the phonetic embeddings extracted from a phonetic classification network, which helps to guide to the attention mechanism with the role of the positional embedding. The addition of a memory layer to a text-dependent SV system was tested on the RSR2015-part II and DeepMine-part I databases, where, in both cases outperformed the baseline result and the reference system based on the same transformer network without the memory layer.
ID documents matching and localization with multi-hypothesis constraints

This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, nonrigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.
The Annoying, the Disturbing, and the Weird: Challenges with Phone Numbers as Identifiers and Phone Number Recycling

Phone numbers are intimately connected to our digital lives. People are increasingly required to disclose their phone number in digital spaces, both commercial and personal. While convenient for companies, the pervasive use of phone numbers as user identifiers also poses privacy, security, and access risks for individuals. In order to understand these risks, we present findings from a qualitative online elicitation study with 195 participants about their negative experiences with phone numbers, the consequences they faced, and how those consequences impacted their behavior. Our participants frequently reported experiencing phone number recycling, unwanted exposure, and temporary loss of access to a phone number. Resulting consequences they faced included harassment, account access problems, and privacy invasions. Based on our findings, we discuss service providers’ faulty assumptions in the use of phone numbers as user identifiers, problems arising from phone number recycling, and provide design and public policy recommendations for mitigating these issues with phone numbers.
Nukabot: Design of Care for Human-Microbe Relationships

Human-computer interaction (HCI) researchers have explored designs that connect humans and non-human beings based on post-humanistic discussions on speculative ethics of care regarding more-than-human worlds. Following these empirical and theoretical frameworks, this paper explores the potential of HCI design to foster human affective emotion toward fermentative microbes. We present the design process for the Nukabot, which is a technologically enhanced traditional Japanese wooden bucket used to pickle vegetables using lactic acid bacteria; the Nukabot is able to have conversations with humans via voice interaction. We describe the ethnographic accounts of six participants who spent 10 days taking care of, talking to, and being addressed by the Nukabot. We analyze their experiences through three ethopoietic elements of care: maintenance, affection, and obligation. Finally, we discuss the design implications of the Nukabot and its contributions to HCI research.
Hardening cryptographic operations through the use of secure enclaves

With the rising popularity of the cloud, companies lose control of both the hardware and the operating system responsible for hosting their software and data. This means that companies are at risk of losing confidential data when these are utilized in components controlled by a third-party cloud vendor. Secure enclaves can help solve this problem by creating a secure environment where code can be executed securely, guaranteeing that no unwanted parties read or modify the data inside this secure environment. While the use of secure enclaves has been focused on small footprints software, such as the implementation of trusted computing base for distributed protocols, we analyze the strengths and shortcoming of current tools in an effort to further expand the applicability of their use. Given the importance of web servers and their inherent greater exposure to attacks, we explore the hardening of Apache web server through the use of secure enclaves. This was accomplished by making the necessary modifications to further protect its private key from both the operating system and hypervisor. We also provide a performance assessment to quantify the overhead associated with the use of secure enclaves, namely, Intel SGX.
Modelling 3D saccade generation by feedforward optimal control

An interesting problem for the human saccadic eye-movement system is how to deal with the degrees-of-freedom problem: the six extra-ocular muscles provide three rotational degrees of freedom, while only two are needed to point gaze at any direction. Measurements show that 3D eye orientations during head-fixed saccades in far-viewing conditions lie in Listing’s plane (LP), in which the eye’s cyclotorsion is zero (Listing’s law, LL). Moreover, while saccades are executed as single-axis rotations around a stable eye-angular velocity axis, they follow straight trajectories in LP. Another distinctive saccade property is their nonlinear main-sequence dynamics: the affine relationship between saccade size and movement duration, and the saturation of peak velocity with amplitude. To explain all these properties, we developed a computational model, based on a simplified and upscaled robotic prototype of an eye with 3 degrees of freedom, driven by three independent motor commands, coupled to three antagonistic elastic muscle pairs. As the robotic prototype was not intended to faithfully mimic the detailed biomechanics of the human eye, we did not impose specific prior mechanical constraints on the ocular plant that could, by themselves, generate Listing’s law and the main-sequence. Instead, our goal was to study how these properties can emerge from the application of optimal control principles to simplified eye models. We performed a numerical linearization of the nonlinear system dynamics around the origin using system identification techniques, and developed open-loop controllers for 3D saccade generation. Applying optimal control to the simulated model, could reproduce both Listing’s law and and the main-sequence. We verified the contribution of different terms in the cost optimization functional to realistic 3D saccade behavior, and identified four essential terms: total energy expenditure by the motors, movement duration, gaze accuracy, and the total static force exerted by the muscles during fixation. Our findings suggest that Listing’s law, as well as the saccade dynamics and their trajectories, may all emerge from the same common mechanism that aims to optimize speed-accuracy trade-off for saccades, while minimizing the total muscle force during eccentric fixation.
Is it worth it? The costs and benefits of bringing a laptop to a university class

Students often bring laptops to university classes, however, they do not limit their laptop use to class-related activity. Off-task laptop use occurs frequently in university classrooms and this use negatively impacts learning. The present study addresses whether potential benefits of class-related laptop use might mitigate the costs of off-task laptop activity. We used tracking software to monitor both class-related and off-task laptop use by undergraduate students enrolled in an introductory psychology course, and we observed how types of laptop use related to course performance. We found a positive correlation between class-related use and exam scores that was driven by viewing lecture slides during class. We also found a negative correlation between off-task laptop use and exam scores, but class-related activities did not predict an increase in off-task use. Thus, for students who constrain their laptop use to class-related activity, the benefits outweigh the costs. While a laptop may be beneficial for some, it is unclear which students are able to constrain themselves to class-related activities and whether the benefits of class-related laptop use obtained by slide viewing could be achieved by other means. Thus, students and educators should carefully consider the costs and benefits of laptop use in the classroom.
Sum-Rank BCH Codes and Cyclic-Skew-Cyclic Codes

In this work, cyclic-skew-cyclic codes and sum-rank BCH codes are introduced. Cyclic-skew-cyclic codes are characterized as left ideals of a suitable non-commutative finite ring, constructed using skew polynomials on top of polynomials (or vice versa). Single generators of such left ideals are found, and they are used to construct generator matrices of the corresponding codes. The notion of defining set is introduced, using pairs of roots of skew polynomials on top of poynomials. A lower bound (called sum-rank BCH bound) on the minimum sum-rank distance is given for cyclic-skew-cyclic codes whose defining set contains certain consecutive pairs. Sum-rank BCH codes, with prescribed minimum sum-rank distance, are then defined as the largest cyclic-skew-cyclic codes whose defining set contains such consecutive pairs. The defining set of a sum-rank BCH code is described, and a lower bound on its dimension is obtained. Thanks to it, tables are provided showing that sum-rank BCH codes beat previously known codes for the sum-rank metric for binary 2 ×2 matrices (i.e., codes whose codewords are lists of 2 ×2 binary matrices, for a wide range of list lengths that correspond to the code length). Finally, a decoder for sum-rank BCH codes up to half their prescribed distance is obtained.
Steganography: Step Towards Security and Privacy of Confidential Data in Insecure Medium by Using LSB and Cover Media

Today, in the digital world, all the things are available on internet. The main concern is about privacy and security due to which steganography has experienced a great concentration by the researcher, as it has been an effective method to provide data security or secure communication. There are many modern and successful methods to secure digital data in a real-world application. Cost effective secrecy can be accomplished by doing the steganography methods. This paper presents the Least Significant Bit Steganography method of embedding text data in an audio file. The text will be taken as a secret message. In second case, I have taken text as a cover media and secret message itself hidden in cover media. The stegno function is based on index location. The key is used to encrypt the stegno function and send it to the receiver end. The stegno function is encoded in such a way that it can be send to the destination safely and to give a better, extremely organized method for maintaining security.
A Novel Mirrored Binocular Vision Sensor Based on Spherical Catadioptric Mirrors

The binocular vision sensor composed of dual cameras is difficult to meet the requirements of synchronous image acquisition in dynamic high-speed measurement, and mirrored binocular vision sensor (MBVS) composed of single camera and catadioptric components have become popular among machine vision applications. Existing mirrored binocular vision sensors mostly comprise planar mirrors, which has the disadvantage of a small field-of-view (FOV). To extend the measurement range of the MBVS, we proposed a novel MBVS based on spherical catadioptric mirrors. The novel MBVS consists of a single camera and double spherical mirrors. The mathematical formula of the FOV of the MBVS is deduced, and the imaging light path is simulated. Then, the structural parameters of the sensor are calculated and a MBVS with a single camera and double spherical mirrors are designed. In order to preliminarily realize the distortion correction of spherical reflection imaging, the mathematical model of the MBVS is constructed. The accuracy of the calibration before and after spherical distortion correction is compared. Experimental results demonstrate the feasibility of the designed MBVS, which can extend the measurement range of the sensor.