Reviewerly/exHarmony huggingface dataset:
Technology-Assisted Social Reforms and Online Hate Content: Insights from India

In the current scenario while everything seems digitalized, we often spend more time scrolling across various social platforms as compared to what we spend in any other real life activities.It becomes a matter of great concern when it comes to analyze what we see and how it is interpreted.Through this paper we aims to identify the influence of technology assisted social-reform initiatives on gender-based hate content generation.With the help of Twitter API, 112577 government-initiated and 58370 citizen-initiated movement(s) tweets have been extracted.This collected data is examined for hatred nature content in terms of emotions using a software programmed in R programming language, the scores for each emotion is counted and a comparison between both the moments is made.The study clearly shows that the Citizeninitiated moments shares comparatively more hate content than the Government-initiated movements as the scores particular to specific emotions like anger, disgust, and sadness is more.This cognitive study can be helpful in policy making, promoting gender based equality, defining strategies to rebuild citizen initiatives in a hate-free environment and controlling hate content generation.
Bit Incorporated Codon Positional Encoder (BICPE) Algorithm for Privacy Preservation in Vertically Partitioned Data in Cloud

Current days an enormous amount of data is put away and recovered from the cloud because of its benefits like stowage size, assets pooling, and multi-occupancy.Because every administration is accessible through cyberspace, there is a lot of ambiguity about security and protection at various levels.This work proposed a privacy-preserving technique by applying Deoxyribonucleic Acid (DNA) coding-based novel Bit Incorporated Codon Positional Encoder (BICPE) algorithm for vertically divided cloud information.The transactional items are scrambled with a replacement of the DNA codon.Fictitious exchanges are appended unilaterally into the private database before encryption by the data owner to alleviate frequency analysis attacks of the items.The proposed BICPE algorithm encrypts the transactional data into A, C, G, T codons to upgrade the safety of the transactional items by replacement, operation, and intricacy.The concepts of binary coding and random values are utilized in DNA sequencing to encode the data.In this method, both the sender and the receiver will share a common DNA sequence for encryption and decryption process.The performance of the proposed technique is compared with the traditional Homomorphic Encryption (HE) in terms of support threshold values (Ts) for different items in a transaction.The exploratory outcomes demonstrate that the BICPE algorithm outperforms HE as far as ciphertext size, execution time, and privacy.Hence, the newly proposed technique is more efficient and offers better performance in terms of privacy. HIGHLIGHTS• Fake transactions are inserted into database to hide item frequency.• The input binary bits are grouped into N sets with length as four for encryption.• Each item in a data set is encoded with A,C,G,T DNA codons.• Encrypted Association rules are built for frequent items in a transaction.
Leveraging Search History for Improving Person-Job Fit

As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates. However, existing studies mainly focus on the recommendation scenario, while neglecting another important channel for linking positions with job seekers, i.e. search. Intuitively, search history contains rich user behavior in job seeking, reflecting important evidence for job intention of users. In this paper, we present a novel Search History enhanced Person-Job Fit model, named as SHPJF. To utilize both text content from jobs/resumes and search histories from users, we propose two components with different purposes. For text matching component, we design a BERT-based text encoder for capturing the semantic interaction between resumes and job descriptions. For intention modeling component, we design two kinds of intention modeling approaches based on the Transformer architecture, either based on the click sequence or query text sequence. To capture underlying job intentions, we further propose an intention clustering technique to identify and summarize the major intentions from search logs. Extensive experiments on a large real-world recruitment dataset have demonstrated the effectiveness of our approach.
Traditional and adaptive learning paradigms as integral components of the innovation-oriented education of the future

Introduction. Traditional education is often opposed to other approaches as some kind of unacceptable extreme. Meanwhile, traditional education represents the nature of a person's creative potential, where both reproductive and creative aspects are significant. The task of innovation-oriented education is due to the balance of the noted aspects of creativity in the context of the content of education, its temporal and other limitations, and, most importantly, the actual as opposed to the declared demand of society for the creative qualities of a citizen, primarily his or her capacity for breakthrough innovations. The purpose of the study is to substantiate the theoretical and practical foundations of innovation-oriented education. Methods. The legacy of John Amos Comenius, the "flipped learning" paradigm in its evolutionary development according to the creative and co-creative methodological foundations of the author’s technology and the "adapting learning" paradigm, a model of education in the discourse of an elitist socio-cultural reality are used herein as a methodological basis for the training of an innovative specialist. Results. The author's technology of "adaptive learning" is revealed. It provides for the organization of training in the form of an unconventional sequence of classes. At the same time, practical or seminar classes held before the lecture are aimed at self-actualization of the educational material problems by the trainee. Adaptive learning can be perceived as a modern version of the "flipped classroom" technology ("flipped learning"), which, under conditions of blended learning, becomes the dominant paradigm of education. It is recommended to widely introduce the adaptive learning technology (paradigm) based on the creativity and co-creation methodology. Conclusion. In the future, the academic lecture inherent in traditional education will act in the system of higher education as a significant feature of an advanced educational institution. The key features of innovation-oriented educational activity are associated with harmoniously combined reproductive and creative manifestations of the learning process, being determined by the coordinated change of the teacher and student. They are conditioned by the search, self-actualized, "problematically matured" cognitive activity of the student. These features characterize a promising technology (paradigm) of "adaptive learning".
Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions

This paper presents a novel planning and control strategy for competing with multiple vehicles in a car racing scenario. The proposed racing strategy switches between two modes. When there are no surrounding vehicles, a learning-based model predictive control (MPC) trajectory planner is used to guarantee that the ego vehicle achieves better lap timing performance. When the ego vehicle is competing with other surrounding vehicles to overtake, an optimization-based planner generates multiple dynamically-feasible trajectories through parallel computation. Each trajectory is optimized under a MPC formulation with different homotopic Bezier-curve reference paths lying laterally between surrounding vehicles. The time-optimal trajectory among these different homotopic trajectories is selected and a low-level MPC controller with control barrier function constraints for obstacle avoidance is used to guarantee system's safety-critical performance. The proposed algorithm has the capability to generate collision-free trajectories and track them while enhancing the lap timing performance with steady low computational complexity, outperforming existing approaches in both timing and performance for a autonomous racing environment. To demonstrate the performance of our racing strategy, we simulate with multiple randomly generated moving vehicles on the track and test the ego vehicle's overtake maneuvers.
A stochastic algorithm for solving the posterior inference problem in topic models

Latent Dirichlet allocation (LDA) is an important probabilistic generative model and has usually used in many domains such as text mining, retrieving information, or natural language processing domains. The posterior inference is the important problem in deciding the quality of the LDA model, but it is usually non-deterministic polynomial (NP)-hard and often intractable, especially in the worst case. For individual texts, some proposed methods such as variational Bayesian (VB), collapsed variational Bayesian (CVB), collapsed Gibb’s sampling (CGS), and online maximum a posteriori estimation (OPE) to avoid solving this problem directly, but they usually do not have any guarantee of convergence rate or quality of learned models excepting variants of OPE. Based on OPE and using the Bernoulli distribution combined, we design an algorithm namely general online maximum a posteriori estimation using two stochastic bounds (GOPE2) for solving the posterior inference problem in LDA model. It also is the NP-hard non-convex optimization problem. Via proof of theory and experimental results on the large datasets, we realize that GOPE2 is performed to develop the efficient method for learning topic models from big text collections especially massive/streaming texts, and more efficient than previous methods.
PRO-VAS: utilizing AR and VSLAM for mobile apps development in visualizing objects

The development of mobile apps with augmented reality (AR) would enhance the capability in visualizing the scene or environment. Any apps supported by computer aided design versions with 3D models makes the design more realistic, such as in the form of websites or mobile apps. However, the current features for online platforms for shopping are quite limited and lack 3D visualization features. This paper presents the development of a mobile application, pro-visualizer app called PRO-VAS, that utilizes AR for scanning and visualizing the environment. PRO-VAS acts as a product visualizer that applies visual simultaneous localization and mapping (VSLAM) for localization of the product in AR based systems. The main components of PRO-VAS are ARCore from Google for interactive purposes, and the depth mapping from red green blue depth (RGB-D) phone camera with point plane generator and markerless tracking method. The last component of the app is the set of objects from the unity store, which can be chosen in PRO-VAS for the scanned scene area. The app was tested in various environments involving different objects and has shown competitive results. In the future, more features and products can be added to the apps.
A Face Recognition Method Based on Multifeature Fusion

Face recognition is widely used in daily life and has an important supporting role for social management. Face recognition is mainly based on historical accumulation data to confirm people’s identities in unknown samples and obtain valuable intelligence information. For the problem of face recognition, this paper proposes a multifeature joint adaptive weighting algorithm framework. In this method, a number of different types of features are first used to describe the face characteristics. The selected features should be as complementary as possible, and the overlap redundant information should be reduced to the greatest extent, so as to ensure the performance and efficiency of multifeature fusion. In the classification stage, based on the joint sparse representation model, the multiple types of features are characterized, and their reconstruction error vectors for the corresponding features of the test sample are calculated. The joint sparse representation model can examine the correlation between different types of features, thereby improving the accuracy of representation and fully integrating the advantages of multiple types of features. At the same time, in view of the simple superposition of reconstruction errors in the traditional sparse representation model, this paper uses a random weight matrix to comprehensively consider the weighted reconstruction errors under different weight conditions, so as to obtain statistical decision quantities for the final decision. The framework proposed in this paper can adapt to different multifeature combinations and has good practicability. In the experiment, training and test sets are constructed based on public face image data sets to test the proposed method. The experimental results show that the method in this paper is more effective and robust compared with some present methods for face recognition.
An improved fish swarm algorithm to assign tasks and cut down on latency in cloud computing

Various researches have been conducted to discover the machinery that led to the evolvement of non-symmetric formation of groups by uncountable marine animals. The huge of tasks comes per unit of time brought obstacles to assign each to particular server, while task assignment have needed a fast strategy to make decision. Artificial fish affect the environment through their behavior and the behavior of their peers. Creating a synthetic fish model has two parts: variables and functions which could be used for task assignment. This paper present improved fish swarm algorithm (IFSA) for task assignment to reduce the latency in cloud computing that could achieve one green computing goals. The research trying to reduce the pending job numbers compared with exist research.
Building and Bridging Security and Privacy-Related Technical Knowledge Amongst HR Professionals

An ever-increasing range of smart, connected internet of things (IoT) devices poses entirely novel security and privacy challenges. Business models that wish to rely on smart product adoption will need to ensure the capability to deliver systems that offer adequate data integrity of sensors with the guarantee of privacy for users. Incorporating smart devices into the mainstream internet poses many security problems, as most internet technologies and protocols have not been developed to support IoT. Strategic support units such as human resources need sufficient technical knowledge about the state of the art to navigate and leverage Industry 4.0 into their practices. Human resources professionals are increasingly asked to take strategic and not operational roles. Using a systematic review method, the authors expose the security and privacy challenges posed by IoTs and conclude with a framework that maps potential solutions to the identified problems. The framework adds to the strategic HR literature and practically helps improve the technical acumen of human resource professionals.
MDS codes over finite fields

The mds (maximum distance separable) conjecture claims that a nontrivial linear mds $[n,k]$ code over the finite field $GF(q)$ satisfies $n \leq (q + 1)$, except when $q$ is even and $k = 3$ or $k = q- 1$ in which case it satisfies $n \leq (q + 2)$. For given field $GF(q)$ and any given $k$, series of mds $[q+1,k]$ codes are constructed. Any $[n,3]$ mds or $[n,n-3]$ mds code over $GF(q)$ must satisfy $n\leq (q+1)$ for $q$ odd and $n\leq (q+2)$ for $q$ even. For even $q$, mds $[q+2,3]$ and mds $[q+2, q-1]$ codes are constructed over $GF(q)$. The codes constructed have efficient encoding and decoding algorithms.
Ensuring Robot-Human Safety for the BD Spot Using Active Visual Tracking and NMPC With Velocity Obstacles

When humans and robots operate in and occupy the same local space, proximity detection and proactive collision avoidance is of high importance. As legged robots, such as the Boston Dynamics (BD) Spot, start to appear in real-world application environments, ensuring safe robot-human interactions, while operating in full autonomy mode, becomes a critical gate-keeping technology for trust in robotic workers. Towards that problem, this article proposes a track-and-avoid architecture for legged robots that combines a visual object detection and estimation pipeline with a Nonlinear Model Predictive Controller (NMPC) based on the Optimization Engine, capable of generating trajectories that satisfy the avoidance and tracking problems in real-time operations. The system is experimentally evaluated using the BD Spot, in a custom sensor and computational suite, in fully autonomous operational conditions, for the robot-human safety scenario of quickly moving noncooperative obstacles.
Correction to “A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction”

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Quantum Walk Experiments Using a 32 × 32 Silicon Photonic Path-Independent Insertion Loss Switch as a Stable Multiport Interferometer

We study a 32 × 32 silicon photonic path-independent insertion loss switch used as a programmable multiport interferometer, with a robustness to optical losses appropriate for many classical and quantum photonic applications since any route through the interferometer has the same loss. Its operation stability was investigated by monitoring the fidelity (closeness to the ideal case) of single-photon quantum-walk experiments, where heralded single photons with a 10-nm bandwidth centered at 1547 nm were used as walkers in the interferometer. Fidelities of 0.98 and 0.97 were observed for 4- and 8-step quantum walks, respectively. The experiment took 21 hours to collect the data for the fidelity calculations. Similar fidelities were confirmed using narrowband classical light sources (200-kHz linewidth), for which a fidelity degradation as small as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-5}$</tex-math></inline-formula> /hour was achieved in the 45-hour free-running operation. The experiments demonstrate that our optical circuit has a wide range of potential applications to classical and quantum photonic processors based on a multiport input–output interference design.
The ASHES 2020 special issue at JCEN

Abstract This editorial provides its readers with a brief introductory overview of the ASHES workshop series in general, and of this special ASHES 2020 issue at the Journal of Cryptographic Engineering (JCEN) in particular.
Strongly symmetric spectral convex bodies are Jordan algebra state spaces

We show that the strongly symmetric spectral convex compact sets are precisely the normalized state spaces of finite-dimensional simple Euclidean Jordan algebras and the simplices. Spectrality is the property that every state has a convex decomposition into perfectly distinguishable pure states; strong symmetry is transitivity, for each integer N, of the affine automorphism group of the state space on lists of N perfectly distinguishable pure states. Additional assumptions combine with this theorem to give simple characterizations of finite-dimensional complex quantum state space. Important aspects of quantum and classical thermodynamics and of query complexity have been generalized to classes of general probabilistic theories (GPTs) satisfying natural postulates including or implying spectrality and strong symmetry; our result shows that these apply to a narrower class of theories than might have been hoped. Sorkin's notion of irreducibly k-th order interference has been studied in the GPT framework and looked for in experiments. Our result shows that the assumption of no higher-order (k > 2) interference, used along with spectrality and strong symmetry to characterize the same class of Jordan-algebraic convex sets by Barnum, Mueller, and Ududec in arXiv:1403.4147, was superfluous. It also implies that Lee and Selby's extension, on the assumption that interference has fixed maximal degree k, of the important order square root of N lower bound on the quantum black-box query complexity of searching N possibilities for one having a desired property (which is achieved by Grover's quantum algorithm), to a class of theories satisfying certain postulates allowing the formulation of a generalized notion of query algorithm, actually applies in the Jordan-algebraic setting where higher-order interference is not possible.
Toward Probabilistic Checking against Non-Signaling Strategies with Constant Locality

Non-signaling strategies are a generalization of quantum strategies that have been studied in physics over the past three decades. Recently, they have found applications in theoretical computer science, including to proving inapproximability results for linear programming and to constructing protocols for delegating computation. A central tool for these applications is probabilistically checkable proof (PCPs) systems that are sound against non-signaling strategies. In this paper we show, assuming a certain geometrical hypothesis about noise robustness of non-signaling proofs (or, equivalently, about robustness to noise of solutions to the Sherali-Adams linear program), that a slight variant of the parallel repetition of the exponential-length constant-query PCP construction due to Arora et al. (JACM 1998) is sound against non-signaling strategies with constant locality. Our proof relies on the analysis of the linearity test and agreement test (also known as the direct product test) in the non-signaling setting.
A Migratory Near Memory Processing Architecture Applied to Big Data Problems

Servers produced by mainstream vendors are inefficient in processing Big Data queries due to bottlenecks inherent in the fundamental architecture of these systems. Current server blades contain multicore processors connected to DRAM memory and disks by an interconnection chipset. The multicore processor chips perform all the computations while the DRAM and disks store the data but have no processing capability. To perform a database query, data must be moved back and forth between DRAM and a small cache as well as between DRAM and disks. For Big Data applications this data movement in onerous. Migratory Near Memory Servers address this bottleneck by placing large numbers of lightweight processors directly into the memory system. These processors operate directly on the relations, vertices and edges of Big Data applications in place without having to shuttle large quantities of data back and forth between DRAM, cache and heavyweight multicore processors. This paper addresses the application of such an architecture to relational database SELECT and JOIN queries. Preliminary results indicate end-to-end orders of magnitude speedup.