Reviewerly/exHarmony huggingface dataset:
REVIEW OF IMAGE ENCRYPTION AND DECRYPTION TECHNIQUES FOR 2D IMAGES

In the emerging era of Internet, multimedia software and application security of images is of major concern. To offer security to these images encryption is the way for robust security. With Image Encryption it becomes difficult to analyze the image that is communicated over untrusted network. Also it provides security for any unauthorized access. The paper provides an introduction to cryptography and various image encryption techniques are reviewed for 2D images.
SHORT STORY AS A MEDIA FOR MOTIVATING STUDENTS' IMPROVEMENT IN READING

This study informs the use of short story as media for motivating students improvement in reading that is tested in third semester B class at Muhammadiyah University of Bengkulu, based on research in EFL students classrooms state, a good indicator of improved motivation and interest in reading can be used by the short story (Ceylan,2016). This method in this research used experimental methods, based on the research conducted. The result showed there was an improvement in students’ motivation in reading. The mean score of the pre-test was 67,64. Then the mean score of the post-test is 94,11. There was an improvement in students’ scores between pre-test and post-test, it is improved 39,13%. It means the result of the test was satisfactory and show the best achievement. So, it can be concluded that improving students’ motivation in reading by using short stories as media is improved. It means that this technique can be used by other researchers to improve the same reading skill.
Leveraging Entity-Type Properties in the Relational Context for Knowledge Graph Embedding

Knowledge graph embedding aims to embed entities and relations of multi-relational data in low dimensional vector spaces. Knowledge graphs are useful for numerous artificial intelligence (AI) applications. However, they (KGs) are far from completeness and hence KG embedding models have quickly gained massive attention. Nevertheless, the state-of-the-art KG embedding models ignore the category specific projection of entities and the impact of entity types in relational aspect. For example, the entity “Washington” could belong to the person or location category depending on its appearance in a specific relation. In a KG, an entity usually holds many type properties. It leads us to a very interesting question: are all the type properties of an entity are meaningful for a specific relation? In this paper, we propose a KG embedding model TPRC that leverages entity-type properties in the relational context. To show the effectiveness of our model, we apply our idea to the TransE, TransR and TransD. Our approach outperforms other state-of-the-art approaches as TransE, TransD, DistMult and ComplEx. Another, important observation is: introducing entity type properties in the relational context can improve the performances of the original translation distance based models.
Iterative Cross-Lingual Entity Alignment Based on TransC

The goal of cross-lingual entity alignment is to match entities from knowledge graph of different languages that represent the same object in the real world. Knowledge graphs of different languages can share the same ontology which we guess may be useful for entity alignment. To verify this idea, we propose a novel embedding model based on TransC. This model first adopts TransC and parameter sharing model to map all the entities and relations in knowledge graphs to a shared low-dimensional semantic space based on a set of aligned entities. Then, the model iteratively uses reinitialization and soft alignment strategy to perform entity alignment. The experimental results show that, compared with the benchmark algorithms, the proposed model can effectively fuse ontology information and achieve relatively better results.
Prevention of Microarchitectural Covert Channels on an Open-Source 64-bit RISC-V Core

Covert channels enable information leakage across security boundaries of the operating system. Microarchitectural covert channels exploit changes in execution timing resulting from competing access to limited hardware resources. We use the recent experimental support for time protection, aimed at preventing covert channels, in the seL4 microkernel and evaluate the efficacy of the mechanisms against five known channels on Ariane, an open-source 64-bit application-class RISC-V core. We confirm that without hardware support, these defences are expensive and incomplete. We show that the addition of a single-instruction extension to the RISC-V ISA, that flushes microarchitectural state, can enable the OS to close all five evaluated covert channels with low increase in context switch costs and negligible hardware overhead. We conclude that such a mechanism is essential for security.
Accurate News Recommendation Coalescing Personal and Global Temporal Preferences

Given session-based news watch history of users, how can we precisely recommend news articles? Unlike other items for recommendation, the worth of news articles decays quickly and various news sources publish fresh ones every second. Moreover, people frequently select news articles regardless of their personal preferences to understand popular topics at a specific time. Conventional recommendation methods, designed for other recommendation domains, give low performance because of these peculiarities of news articles.
A 2Cat-inspired model structure for double categories

We construct a model structure on the category $\mathrm{DblCat}$ of double categories and double functors. Unlike previous model structures for double categories, it recovers the homotopy theory of 2-categories through the horizontal embedding $\mathbb{H}\colon2\mathrm{Cat}\to\mathrm{DblCat}$, which is both left and right Quillen, and homotopically fully faithful. Furthermore, we show that Lack's model structure on $2\mathrm{Cat}$ is both left- and right-induced along $\mathbb{H}$ from our model structure on $\mathrm{DblCat}$. In addition, we obtain a $2\mathrm{Cat}$-enrichment of our model structure on $\mathrm{DblCat}$, by using a variant of the Gray tensor product. Under certain conditions, we prove a Whitehead theorem, characterizing our weak equivalences as the double functors which admit an inverse pseudo double functor up to horizontal pseudo natural equivalence. This retrieves the Whitehead theorem for 2-categories. Analogous statements hold for the category $\mathrm{wkDblCat}_s$ of weak double categories and strict double functors, whose homotopy theory recovers that of bicategories. Moreover, we show that the full embedding $\mathrm{DblCat}\to\mathrm{wkDblCat}_s$ is a Quillen equivalence.
Constructive feedback of non-Markovianity on resources in random quantum states

We explore the impact of non-Markovian channels on the quantum correlations (QCs) of Haar uniformly generated random two-qubit input states with different ranks -- either one of the qubits (single-sided) or both the qubits independently (double-sided) are passed through noisy channels. Under dephasing and depolarizing channels with varying non-Markovian strength, entanglement and quantum discord of the output states collapse and revive with the increase of noise. We find that in case of the depolarizing double-sided channel, both the QCs of random states show a higher number of revivals on average than that of the single-sided ones with a fixed non-Markovianity strength, irrespective of the rank of the states -- we call such a counter-intuitive event as a constructive feedback of non-Markovianity. Consequently, the average noise at which QCs of random states show first revival decreases with the increase of the strength of non-Markovian noise, thereby indicating the role of non-Markovian channels on the regenerations of QCs even in presence of a high amount of noise. However, we observe that non-Markovianity does not play any role to increase the robustness in random quantum states which can be measured by the mean value of critical noise at which quantum correlations first collapse. Moreover, we observe that the tendency of a state to show regeneration increases with the increase of average QCs of the random input states along with non-Markovianity.
Application of Hyperspectral Remote Sensing in the Longwave Infrared Region to Assess the Influence of Dust from the Desert on Soil Surface Mineralogy

Soil mineralogy can be used to study changes in the environment affecting the soil surface, such as dust from the desert through Aeolian processes, which is one of the sources that determine the mineral nature of the soil. Ground- and field-based hyperspectral longwave infrared images, acquired before and after dust dispersion on the soil surface, were processed and analyzed by applying a procedure for determining soil surface mineralogy from the emissivity spectrum, using two indices―SQCMI (the Soil Quartz Clay Mineral Index) and SCI (the Soil Carbonate Index)―to identify changes in the abundance of quartz, clay minerals and carbonates on the surface, caused by the settling dust particles. Mineralogical changes were identified, depending on the mineral composition of the dust compared to the soil surface mineralogy.
Privacy-Preserving Contact Tracing: current solutions and open questions

The COVID-19 pandemic has posed a unique challenge for the world to find solutions, ranging from vaccines to ICT solutions to slow down the virus spreading. Due to the highly contagious nature of the virus, social distancing is one fundamental measure which has already adopted by many countries. At the technical level, this prioritises contact tracing solutions, which can alert the users who have been in close contact with the infected persons and meanwhile allow heath authorities to take proper actions. In this paper, we examine several existing privacy-aware contact tracing solutions and analyse their (dis)advantages. At the end, we describe several major observations and outline an interdisciplinary research agenda towards more comprehensive and effective privacy-aware contact tracing solutions.
EMOVA: A Semi-supervised End-to-End Moving-Window Attentive Framework for Aspect Mining

Aspect mining or extraction is one of the most challenging problems in aspect-level analysis on customer reviews; it aims to extract terms from a review describing aspects of a reviewed entity, e.g., a product or service. As aspect mining can be formulated as the sequence labeling problem, supervised deep sequence learning models have recently achieved the best performance. However, these supervised models require a large amount of labeled data which are usually very costly or unavailable. To this end, we propose a semi-supervised End-to-end MOVing-window Attentive framework (called EMOVA) that has three key features for aspect mining. (1) Two neural layers with Bidirectional Long Short-Term Memory (BiLSTM) are employed to learn representations of reviews. (2) Cross-View Training (CVT) is used to improve the representation learning over a small set of labeled reviews and a large set of unlabeled reviews from the same domain in a unified end-to-end architecture. (3) Since past nearby information in a text provides important semantic contexts for a prediction task in aspect mining, a moving-window attention component is proposed in EMOVA to enhance prediction accuracy. Experimental results over four review datasets from the SemEval workshops show that EMOVA outperforms the state-of-the-art models for aspect mining.
Aggregation and Finetuning for Clothes Landmark Detection

Landmark detection for clothes is a fundamental problem for many applications. In this paper, a new training scheme for clothes landmark detection: $\textit{Aggregation and Finetuning}$, is proposed. We investigate the homogeneity among landmarks of different categories of clothes, and utilize it to design the procedure of training. Extensive experiments show that our method outperforms current state-of-the-art methods by a large margin. Our method also won the 1st place in the DeepFashion2 Challenge 2020 - Clothes Landmark Estimation Track with an AP of 0.590 on the test set, and 0.615 on the validation set. Code will be publicly available at https://github.com/lzhbrian/deepfashion2-kps-agg-finetune .
An Improved Image Segmentation System

In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation process ensuring efficiency. Our results show that the performance indices in the system were higher. Furthermore, the integration of the cooperation paradigm allows to speed up the segmentation process. Besides, the tests reveal the robustness of our method by proving competitive results. Our proposal achieved an accuracy of 93,51%± 0,8, a sensitivity of 93,53%± 5,08 and a specificity rate of 92,64%± 4,01.
The Effect of Accounting Disclosure about Sustainability Reports on the Ability of Banks to Going Concern - Case Study of Arab Bank

The main objective of the study was to identify the effect of accounting disclosure of sustainability reports on Going Concern in the Arab Bank. To achieve the objective of the study, the content of the sustainability reports issued by Arab Bank were analyzed to identify the disclosure level for the economic, environmental and social dimensions in accordance to the (GRI,2014) initiative indicators. Furthermore, the Going Concern ability of the bank was measured using a set of financial indicators selected based on the results reported in previous literature addressing Continuity in commercial banks. Data used in the current study was taken from the sustainability reports and the annual statements issued by The Arab Bank (2010-2014). The study concluded that there is a significant effect of the disclosure of the dimensions of all the sustainability reporting on the return on assets and the return on equit). The study recommended the need for Arab Bank to disclose the dimensions of sustainability dimension in accordance to the International disclosure Initiative (GRI, 2014).There is also a need for more disclosure of the accounting activities in annual statements to provide a clearer picture for the sustainability accounting statements and annual statements which reflect the Arab Bank concern with respect to sustain the community resources and its ability to Concern Going.
The Methods and Experiments of Shape Measurement for Off-Axis Conic Aspheric Surface

The off-axis conic aspheric surface is widely used as a component in modern optical systems. It is critical for this kind of surface to obtain the real accuracy of the shape during optical processing. As is widely known, the null test is an effective method to measure the shape accuracy with high precision. Therefore, three shape measurement methods of null test including auto-collimation, single computer-generated hologram (CGH), and hybrid compensation are presented in detail in this research. Although the various methods have their own advantages and disadvantages, all methods need a special auxiliary component to accomplish the measurement. In the paper, an off-axis paraboloid (OAP) was chosen to be measured using the three methods along with auxiliary components of their own and it was shown that the experimental results involved in peak-to-valley (PV), root-mean-square (RMS), and shape distribution from three methods were consistent. As a result, the correctness and effectiveness of these three measurement methods were confirmed, which are very useful in engineering.
A Reversible Hiding Technique Using LSB Matching for Relational Databases

Data hiding technique is an important multimedia security technique and has been applied to many domains, for example, relational databases.The existing data hiding techniques for relational databases cannot restore raw data after hiding.The purpose of this paper is to propose the first reversible hiding technique for the relational database.In hiding phase, it hides confidential messages into a relational database by the LSB (Least-Significant-Bit) matching method for relational databases.In extraction and restoration phases, it gets the confidential messages through the LSB and LSB matching method for relational databases.Finally, the averaging method is used to restore the raw data.According to the experiments, our proposed technique meets data hiding requirements.It not only enables to recover the raw data, but also maintains a high hiding capacity.The complexity of our algorithms shows their efficiencies.
Efficiency Problem of renewable energetics systems in the context of «smart house» concept

Considering the question of efficiency of alternative energy systems, producing electricity from the position of interests of households it acquires an unexpected sound. The efficiency can be low (about 7-10%) providing that the heat released in converting is targeted i.e also used on the needs of households. In such an approach low efficiency becomes acceptable since the most part of energy usage in households is spent on systems using the heat (hot water supply etc.) even in the summer period. It is shown that converting of energy providing targeted heat dissipation can be provided by the phenomenon of generation of electromotive force by flowing of the low-speed, low-molecular salt current trough the polymeric sewed net; relevant experimental results are presented. Basing on the analysis of low-molecular ions currents balance by flowing of solution through the gel, the theory of the phenomenon is proposed. It is shown that the described phenomenon and based on its energy converting systems can be considered as an example illustrating the perspectivity of the new paradigm of green energetics development. Given paradigm is based on principles of technical and humanitarian knowledge convergence and is completely consistent with the conclusion that the environment-friendly technologies development is a technical and social issue at one time.
On Black-Box Extensions of Non-interactive Zero-Knowledge Arguments, and Signatures Directly from Simulation Soundness

Highly efficient non-interactive zero-knowledge arguments (NIZK) are often constructed for limited languages and it is not known how to extend them to cover wider classes of languages in general. In this work we initiate a study on black-box language extensions for conjunctive and disjunctive relations, that is, building a NIZK system for $${\mathcal L}\diamond \hat{{\mathcal L}}$$ (with $$\diamond \in \{\wedge , \vee \}$$) based on NIZK systems for languages $${\mathcal L}$$ and $$\hat{{\mathcal L}}$$. While the conjunctive extension of NIZKs is straightforward by simply executing the given NIZKs in parallel, it is not known how disjunctive extensions could be achieved in a black-box manner. Besides, observe that the simple conjunctive extension does not work in the case of simulation-sound NIZKs (SS-NIZKs), as pointed out by Sahai (Sahai, FOCS 1999). Our main contribution is an impossibility result that negates the existence of the above extensions and implies other non-trivial separations among NIZKs, SS-NIZKs, and labelled SS-NIZKs. Motivated by the difficulty of such transformations, we additionally present an efficient construction of signature schemes based on unbounded simulation-sound NIZKs (USS-NIZKs) for any language without language extensions.
RETRACTED: Research on the application of big data in the management and service of College Students

Abstract The explosive growth of information and the maturity and application of cloud computing, data mining and analysis technology mark that today’s society has ushered in the era of big data. College student management and service work are practical, but the traditional way of it is mostly manual and single qualitative management, which has been unable to meet the growing number of students demands, gradually revealing lots of problems like low efficiency, slow speed, and poor quality. The emergence of big data will effectively improve this problem. College students’ education management workers will be able to accurately control the ideological and behavioral dynamics of students, properly carry out public opinion analysis, and promote the personalized learning and development of college students.
Adaptive Graphical Model Network for 2D Handpose Estimation.

In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image. The AGMN consists of two branches of deep convolutional neural networks for calculating unary and pairwise potential functions, followed by a graphical model inference module for integrating unary and pairwise potentials. Unlike existing architectures proposed to combine DCNNs with graphical models, our AGMN is novel in that the parameters of its graphical model are conditioned on and fully adaptive to individual input images. Experiments show that our approach outperforms the state-of-the-art method used in 2D hand keypoints estimation by a notable margin on two public datasets.
Temporal Common Sense Acquisition with Minimal Supervision

Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TACOLM, a temporal common sense language model. Our method is shown to give quality predictions of various dimensions of temporal common sense (on UDST and a newly collected dataset from RealNews). It also produces representations of events for relevant tasks such as duration comparison, parent-child relations, event coreference and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the standard BERT. Thus, it will be an important component of temporal NLP.
The effects of picture word inductive model (PWIM) toward student’s early reading skills of first-grade in the primary school

The difficulties for first-grade students in the primary school in reading, particularly unfamiliarity with the alphabet, obstruct their communication due to the inability to use the right words and proper spelling. These issues lead to low reading comprehension. The use of inappropriate learning models for early reading skills and inaccurate learning media creates difficulties for students in reading. As such, to improve their reading comprehension, the use of PWIM with the emphasis on students' ability to think inductively is considered to be the right solution. This study aims to investigate the influence of PWIM on early reading skills in first-grade students of primary school in Indonesia. This study used a quasi-experimental model with a control group pre-test and post-test. The data analysis techniques involved the T-test and gain score test. The results of the study acknowledged significant difference in reading skills toward early reading skills. PWIM posed a significant influence on the early reading skills of primary students, compared to conventional teaching using textbooks. PWIM was proven influential to empower the inductive thinking of primary school students.