Reviewerly/exHarmony huggingface dataset:
Homomorphic Evaluation of Lightweight Cipher Boolean Circuits

Motivated by a number of applications of lightweight ciphers in privacy-enhancing cryptography (PEC) techniques such as secure multiparty computation (SMPC), fully homomorphic encryption (FHE) and zero-knowledge proof (ZKP) for verifiable computing, we investigate the Boolean circuit complexity of the core primitives of NIST lightweight cryptography (LWC) round 2 candidates. In PEC, the functionalities (e.g., ciphers) are often required to express as Boolean or arithmetic circuits before applying PEC techniques, and the size of a circuit is one of the efficiency factors. As a use case, we consider homomorphic evaluation of the core AEAD circuits in the cloud-outsourcing setting using the TFHE scheme, and present the performance results.
Provinciality of Goldfields

There is a strong tendency for gold ore bodies to be clustered as goldfields, and goldfields to be clustered as provinces. This tendency to occur in provinces is the first of five characteristics that are described and given importance when determining how gold deposits form. Goldfields are clusters up to 10 – 20 km in length. Provinces can be several 100 km, contain multiple goldfields, and indicate one of the scales of study necessary to understand gold deposit formation.
Discriminant Analysis on a Stream of Features

Online learning is a well-established problem in machine learning. But while online learning is commonly concerned with learning on a stream of samples, this article is concerned with learning on a stream on features. A modified quadratic discriminant analysis (QDA) is proposed because it is fast, capable of modeling feature interactions, and it can still return an exact solution. When a new feature is inserted into a training set, the proposed implementation of QDA showed a 1000-fold speed up to scikit-learn QDA. Fast learning on a stream of features provides a data scientist with timely feedback about the importance of new features during the feature engineering phase. In the production phase, it reduces the cost of updating a model when a new source of potentially useful features appears.
Application of Virtual Reality to Instructions of Manual Lifting Analysis

Application of virtual and augmented reality has been popular in various areas such as education, entertainment, training, communication, design, therapy, and more. In education, particularly, virtual reality (VR) has been widely used by researchers or educators to create interactive instructions for interesting problem in alternatively real-like environments. Educators have recognized the potentials of VR to educational instructions in traditional classrooms where the environment of instructions would be limited to two-dimensional space and third-person’s view to a problem. Application of VR can allow students to be placed themselves into a virtual environment of interest with more immersive perception of problem conditions and application of instructions. In the literature, a considerable number of studies have applied digitalization technologies including virtual and augmented reality in design of process of workstations, manufacturing workplaces, and maintenance activities. In general, most of studies evaluated potentials of application of virtual reality from the perspective of fidelity, usability, and cost, but not from the perspective of effectiveness of instructions. In traditional instructions of manual lifting analysis, workplace or task conditions for manual lifting are usually given by statements, sometimes with pictorial descriptions. From the perspective of learners who need to investigate and analyze lifting tasks, it would be necessary to convert two-dimensional descriptions of task conditions into three-dimensional spaces for better understanding of tasks. However, the conversion would be limited with individual’s capability of imaginary perception. Providing virtual task environment for lifting task conditions to learners would be very helpful to understand underlying conditions for lifting task. The objective of this study was to design a teaching aid to improve instructions in virtual reality environments and to compare effectiveness of instructions between traditional instructions and virtual reality instructions. One exemplary lifting task published by Centers for Disease Control and Prevention was designed by using the Unity software. This example may be used by other educators to develop their own virtual instructions for different task analyses.
A Multi-View Ensemble Clustering Approach Using Joint Affinity Matrix

In multi-view ensemble clustering, the correctly-partitioned data objects should be assigned with a higher weight, thereby helping to decrease the influence of incorrectly-partitioned data objects. Therefore, different data objects should be treated separately instead of being assigned the same view weight as traditional solutions. In this paper, a multi-view ensemble clustering approach is proposed using joint affinity matrix. Firstly, a new concept of core data objects is defined according to the influence index and Gaussian Mixed Model, and basic partitions and sample-level weights can be yielded for every view. Secondly, a joint affinity matrix, which maintains pairwise similarities of all data objects, is generated using the sample-level weights. Consequently, data objects can be effectively assigned to the correct partition. Thirdly, a multi-view ensemble clustering algorithm is proposed using the joint affinity matrix. In the end, experimental results on benchmark datasets validate the efficacy of the algorithm with state-of-the-art baselines.
Catch me if you can: achieving complete internet anonymity using open source technologies

Search engines aggressively capture user data for targeted advertisements at the cost of user privacy. We present a framework for complete internet anonymity that blocks search engines from tracking user data based on cookies, IP address and search queries . The framework consists of a browser plugin to enable anonymous search by masking browser cookies, a VPN service to enable masking IP address and finally Natural Language Processing models for creating multiple queries to mask user data. Based completely on open source technologies, the framework serves as the first of its kind open source internet anonymity tool.
Listening First: Egocentric Textual Descriptions of Indoor Spaces for People with Blindness

Orientation in unknown environments is challenging for people with. Especially in indoor environments, there are very few systems that support navigation but almost none for orientation. Thus, we propose a grammar for generating German textual descriptions of indoor environments for users with blindness. We utilize an egocentric approach taking into account the user’s location and orientation in the building. To investigate what word order is preferred, we compare descriptions generated by three different grammars. We also examine what strategies people with blindness pursue to orient themselves in buildings. We test our concept in an online user study with people with blindness. Our study shows that egocentric information should be brief, always following the same structure and allow for customization.
Key Processes and Rituals of Technology Teams

Agile processes break each project into chunks that are manageable by smaller and distributed teams.
Design Research on Financial Risk Control Model Based on CNN

In the new era, with the rapid entry of a large number of financial products into the market environment, network finance has become an indispensable part of people's daily life. From the practical point of view, the network loan has the advantages of simple operation and a short audit cycle, so it has attracted the attention of the public once it is promoted. However, due to the influence of the environment and their own technology, it is easy to appear the risk of fraud during the operation. In the face of this development phenomenon, it is very important for the future development of the internet finance industry to construct a financial risk control model based on the credit evaluation of loan applicants. Based on the understanding of the risk control model in an unbalanced data environment, this paper constructs the design and implementation of the financial risk control model based on the convolutional neural network (CNN), and analyzes and discusses the final results.
THE EFFECTIVENESS OF VOCATIONAL TRAINING IN THE CONTEXT OF DIGITAL TRANSFORMATION: PROBLEMS AND SOLUTIONS

The article is devoted to the problem of increasing the efficiency of training professional personnel in the context of the digital transformation of the education system. In particular, the content of the concepts of digitalization and digital transformation has been clarified and disclosed in relation to the specifics of the field of professional training; disclosed the socio-economic essence of the key problems of the educational process and, in particular, the problem of improving the organizational and economic mechanism of the educational process; content of educational programs; the transition of the education system from reproductive to creatively authorized teaching methods; problems of digital transformation of forms of organization of educational and methodological work; motivation of students to master, acquire knowledge, skills, etc. The direction of the strategic vision of an effective model of digital transformation in the educational sphere is formulated.
Remarks on a Quaternion Neural Network-Based Controller Applied to a Three-Link Robot Manipulator

In this study, the application of a quaternion neural network to control a robot manipulator is investigated. The quaternion neural network is a multilayer feed-forward network with a split-type activation function of neurons and that uses a tapped-delay-line input. In the control system, a feedback error-learning scheme is used to train the network. In the computational experiments, a three-link robot manipulator is controlled using the proposed quaternion neural network-based controller, and the simulation results demonstrate the feasibility of the proposed controller.
Efficient Volume-Hiding Encrypted Multi-Maps with Support for Conjunctive Queries

Structured Encryption (STE), introduced by Chase and Kamara (ASIACRYPT 2010), enables to perform efficient private queries over an encrypted data structure. Very recently, Kamara and Moataz (EUROCRYPT 2019) formalized the notion of volume-hiding STE to mitigate the volume attack, and presented the first concrete volume-hiding encrypted multi-map (EMM) scheme with densest subgraph transform, which can ensure that the volume of values associated with the queried key is hidden to the adversary. To achieve better performance, Patel et al. (CCS 2019) designed a volume-hiding EMM scheme from cuckoo hash dubbed dprfMM. However, we observe that all the existing volume-hiding EMMs focus only on queries over a single key. Although the state-of-the-art conjunctive queries scheme OXT (CRYPTO 2013) can achieve sub-linear search complexity for conjunctive queries, it reveals the real volumes of the queried keys. Therefore, it is desirable to explore the volume-hiding encrypted multi-maps supporting conjunctive queries. In this paper, we initiate the study of volume-hiding EMM supporting conjunctive queries. We present, to our best knowledge, the first volume-hiding conjunctive EMM scheme OXTMM. Compared with the state-of-the-art volume-hiding single-key EMM scheme dprfMM, our proposed OXTMM can guarantee that the real volume of each key is hidden while supporting efficient conjunctive queries, the query communication overhead is $$2\ell $$ encrypted key/value pairs together with a succinct data structure Bloom filter, where $$\ell $$ is the maximum volume of all keys. In addition, we prove the security of our proposed OXTMM and give a thorough efficiency analysis between our proposal and the existing constructions. Finally we implement OXTMM and compare it with the most efficient scheme $$\textsf {OXT}$$ . Experiments result demonstrates that OXTMM can achieve volume-hiding conjunctive queries with a moderate efficiency loss.
Optimizing Anonymity and Performance in a Mix Network

Mix networks were developed to hide the correspondence between senders and recipients of the communication. In order to be usable and defend user privacy, anonymous communication networks like mixnets need to be parameterized in an optimal manner. This work uses a mixnet simulator to determine reasonable packet size and parameters for the real-world Nym mixnet, a stratified continuous-time mixnet that uses the Sphinx packet format. We analyzed network parameters, such as the sending rate, cover traffic overhead, and mixing delay, to determine the impact of various configurations on the anonymity and performance.
Mixture and Latent Class Models: Applications

In this chapter we provide applications of mixture and latent class models. First, two examples of Gaussian mixture models are presented. In the application to the conservation of liquid task (Sect. 3.2), an illustration is provided of the occurrence of local maxima in the log likelihood, which is a recurring challenge in mixture and latent class models. Section 3.3 extends this analysis to a multivariate Gaussian mixture model and illustrates some of the practical difficulties in estimating such models. In Sect. 3.4, a latent class model is presented for the balance scale data, consisting of four binary items. This example is then used to illustrate testing item homogeneity: are the items equivalent to each other? Given such item homogeneity, items can then be collapsed into a sum score. Hence, the application in Sect. 3.5 analyses such sum scores of items using binomial mixtures, and illustrates testing direct versus indirect effects of age. The final application (Sect. 3.6) concerns data from the dimensional change card sort task, also by binomial mixture models, and illustrates the use of the bootstrapped likelihood ratio statistic for model selection. Note that the term latent class model is mostly used for mixture models for multivariate categorical or nominal data. An example is provided with multivariate binary data in Sect. 3.4.
Identifying, Evaluating, and Addressing Nondeterminism in Mask R-CNNs

Convolutional Neural Networks, and many other machine learning algorithms, use Graphical Processing Units (GPUs) instead of Central Processing Units (CPUs) to improve the training time of very large modeling computations. This work evaluates the impact of the model structure and GPU on nondeterminism and identifies its exact causes. The ability to replicate results is quintessential to research, thus nondeterminism must be either removed or significantly reduced. Simple methods are provided so that researchers can: (1) measure the impact of nondeterminism, (2) achieve determinable results by eliminating randomness embedded in the model structure and performing computations on a CPU, or (3) reduce the amount of variation between model performances while training on a GPU.
Some Aspects of Heat Sensing by Slices of Carrot and Radish Through Electrical Resistance as a Tool for Signal Transduction

Heat sensed by the porous part of two common vegetables, namely carrot (Daucus carota) and radish (Raphanus sativas) studied with the aid of temperature dependent electrical resistance measurements is described. The resistance of the thin slices showed a variation of electrical resistance with temperature that increased in the resistance with temperature and decreased from a particular temperature while the measurements were carried out in the temperature varying from 30 °C to 100 °C. The differential scanning calorimetry, thermogravimetry and optical microscopic studies had revealed the textures and temperatures of loss of the water molecules during heating. The electrical conductivity of the thin slices after treatment with different alkali metal salts such as KCl, NaCl, KI showed that the difference in resistance electrical resistance profile with respect to the original sample, whereas at the high concentration of the salts the resistance remained constant within the range 30 °C to 100 °C. A semi-quantitative explanation on the sensing through electrical resistance signal transduction of the two biological porous samples was presented.
Targeting Targeted Harassment: Problems with Criminalization and Platform Liability

While there are plenty of ways to tackle content-related harms in the online environment, the phenomenon of targeted harassment of individuals presents particular regulatory challenges. Re-gulatory means that may be justified and effective against clearly illegal content (e.g., child sexual abuse material or certain forms of hate speech) may unduly restrict freedom of expression and/or be inefficient in relation to targeted harassment. In this paper, I explore why this subcategory of harmful online speech may be especially difficult to prevent through the traditional means of criminal law and rules governing liability for third party content. Although these challenges are considerable, I identify approaches that may help to counter targeted harassment or to reduce its harms.
Application of Cloud Desktop Technology in Computer Laboratory Management

The development of computer network technology to promote the development of all walks of life, and the rapid development of cloud computing industry is to let a lot of industry see the advantages of the development of network technology, even in COVID - 19 outbreak of the moment, still did not stop the development of cloud computing industry, whether it is a traditional industry, or for the computer industry, Cloud management, cloud services and cloud technology are the main directions of future development. With the rapid development of Internet and information technology, cloud desktop technology based on cloud service technology is becoming more and more mature, and provides strong support for modern mobile office. For the information teaching of colleges and universities, the computer laboratory as the main means at any time should pay attention to the improvement of the degree of information teaching, and the emergence and application of cloud desktop technology can effectively solve the drawbacks of traditional computer laboratory management. This paper focuses on the application of cloud desktop technology in the scientific management of university computer laboratories.
How to Support Rapid Prototyping of Virtual Content for Automotive AR Head-Up Displays?

Emerging AR applications for vehicles, and especially for AR HUDs, are current topics for modern assistance systems to support drivers in their driving tasks. Prototyping of AR experiences is presented in current research in immersive authoring tools to overcome complexities and entry barriers for designers and psychologists. However, these are not applicable to navigation displays for AR HUD. This paper presents a qualitative study with concept developers in their domain to determine the characteristics of AR applications that the HUD should possess. The different dimensions of the problems and properties of the required information are presented. The different facets of associated issues and information requirements are also discussed. These facets and its corresponding information are categorized in different levels of complexity of the navigation context, which are not typically part of the development of standard AR applications. Technological entry barriers introduce additional complexity levels for the concept developers. More specifically, the representation of dynamic virtual objects on displays in dynamic environments complicates the development as there is no suitable evaluation platform meeting the requirements of the concept developers.
A Stochastic Modified Limited Memory BFGS for Training Deep Neural Networks

In this work, we study stochastic quasi-Newton methods for solving the non-linear and non-convex optimization problems arising in the training of deep neural networks. We consider the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update in the framework of a trust-region approach. We provide an almost comprehensive overview of recent improvements in quasi-Newton based training algorithms, such as accurate selection of the initial Hessian approximation, efficient solution of the trust-region subproblem with a direct method in high accuracy and an overlap sampling strategy to assure stable quasi-Newton updating by computing gradient differences based on this overlap. We provide a comparison of the standard L-BFGS method with a variant of this algorithm based on a modified secant condition which is theoretically shown to provide an increased order of accuracy in the approximation of the curvature of the Hessian. In our experiments, both quasi-Newton updates exhibit comparable performances. Our results show that with a fixed computational time budget the proposed quasi-Newton methods provide comparable or better testing accuracy than the state of the art first-order Adam optimizer.
Artificial Intelligence (AI) Strategies for Face Mask Recognition and Facial Authenticity

The COVID-19 outbreak has wreaked havoc upon our regular lifestyle, impacting world commerce & transportation. Protecting person's face by wearing a face mask seems to have become the accepted norm. Several organisations will soon need consumers to wear appropriate masks in order to use respective facilities. As a corollary, detecting face masks became a key task in the service of global civilisation. This report explains how to achieve these goals using basic Machine Learning technologies as an example, TensorFlow, Keras, OpenCV, also, Scikit-Learn. A biometric system has been developed to assess if or not there is a human in an image. The proposed method effectively distinguishes the face clearly or a clip and thereafter assesses if it is covered by a mask. This can discern a face with a mask in action and also in a clip as a monitoring employee productivity. The approach achieves a high level of precision. The optimum settings again for Convolutional Neural Network model (CNN) that identify the existence of any filters despite causing prediction error.
SSFuzzyART: A Semi-Supervised Fuzzy ART Through Seeding Initialization

Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labelled data is available. Indeed, labelled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labelled data. This paper introduces a semi-supervised variant of the Fuzzy ART clustering algorithm (SSFuzzyART). The proposed solution uses the available labelled data to initialize clusters centers. A set of experiments is carried out on some available benchmarks. SSFuzzyART demonstrated better clustering prediction results than its classic counterpart.
A CNN Based Visual Audio Steganography Model

Deep learning based steganography is an important protection for secret message, especially for secret images. For different type of cover and secret message, such as audio cover and secret image, the imperceptibility of steganography can be improved, however, the representation difference between audio cover and secret image becomes a great challenge. In this paper, we propose a visual audio steganography model to based on convolutional neural network (CNN). In our model, we design an audio visualization method with STFT and DWT transformation. Then we exploit ISGAN to build an auto encoder, in order to embed a grayscale image into a segment of audio in the embedding stage. Experimental results show that generated stego audio fidelity is indistinguishable to the listener, and we can extract high-quality grayscale images from stego audio in the extraction stage.