Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses

3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. Several previous works addressed the inverse problem by generating multiple hypotheses. However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be difficult to obtain. In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences. To this end, we design our network to model a proposal distribution which we use to approximate the unknown multi-modal target posterior distribution. We achieve the approximation by minimizing the KL divergence between the proposal and target distributions, and this leads to a 2D reprojection error and a prior loss term that can be weakly supervised. Furthermore, we determine the most probable solution as the conditional mode of the samples using the mean-shift algorithm. We evaluate our method on three benchmark datasets -- Human3.6M, MPII and MPI-INF-3DHP. Experimental results show that our approach is capable of generating multiple feasible hypotheses and achieves state-of-the-art results compared to existing weakly supervised approaches. Our source code is available at the project website.
EMPLOYEE COMMITMENT ON ORGANIZATION PERFORMANCE AT TIMAFLOR COMPANY LIMITED IN LAIKIPIA COUNTY, KENYA

The general objective of this study was to investigate the effects of employee commitment on the performance of Timaflor Limited Company. Specific Objectives were to determine the effect of employees training, employee compensation, employee continuance involvement and working conditions on performance of Timaflor Limited Company. The study used resource based view theory and Allen and Meyer Multidimensional theory. This study adopted descriptive research design. The target population of the study was 340 employees working at Timaflor Limited Company. The respondents were selected from the Company’s four departments. The study used proportionate stratified random sampling to determine the number of staff to participate in the study. Sample size was 103 staff distributed in four departments. The study utilized primary data that was collected by use of questionnaires that was administered to staff. Piloting was done at Kariki flower farm in Naivasha, Nakuru County. The Content Validity Index (CVI) was used to test validity of the research instruments. The research instruments were also tested for reliability by computing the Cronbach Alpha Statistical Tests. Quantitative data collected were analyzed using descriptive statistical techniques which were frequencies, mean, standard deviation. Inferential statistics and Regression Analysis Model were also employed. Data was presented in tables. The findings showed that working conditions, training, compensation and continuance employee involvement affected organization performance. Findings showed that training, compensation and working conditions have a positive and significant effect on organization performance. The study recommended that the training programmes should be practical and related to the actual challenges at work. It also recommended that flower firms give employees opportunities for career advancement. Lastly, there is need for the organizations to improve pay and benefits so as to motivate employees to remain committed to the organization. Keywords: Employees Training, Compensation, Continuance Involvement, Working Conditions CITATION: Ndai, J. N., &amp; Makhamara, F. (2020). Employee commitment on organization performance at Timaflor Company Limited in Laikipia County, Kenya. The Strategic Journal of Business &amp; Change Management, 8 (1), 132 – 149.
Identifying Informative Latent Variables Learned by GIN via Mutual Information

How to learn a good representation of data is one of the most important topics of machine learning. Disentanglement of representations, though believed to be the core feature of good representations, has caused a lot of debates and discussions in recent. Sorrenson et al. (2020), using the techniques developed in nonlinear independent analysis theory, show that general incompressible-flow networks (GIN) can recover the underlying latent variables that generate the data, and thus can provide a compact and disentangled representation. However, in this paper, we point out that the method taken by GIN for informative latent variables identification is not theoretically supported and can be disproved by experiments. We propose to use the mutual information between latent variables and the auxiliary variable to correctly identify informative latent variables. We directly verify the improvement brought by our method in experiments on synthetic data. We further show the advantage of our method on various downstream tasks including classification, outlier detection and adversarial attack defence.
Changes for the better? A perspective based on post-secondary “Literature in English” in Malaysia

This chapter is premised on the idea that an exploration of literary text selection and expected demonstrations of reading practices in literature education will provide a richly complex and necessary perspective in discussions on literacy in Asia. Freebody’s Four Resources model for literacy are applied to examine literature education in the form of post-secondary “Literature in English” as a Malaysian school subject. Of particular interest is the subject’s assessment, which has undergone four phases of change, each in line with a reconceptualisation of literature education. Those changes, as well as interviews with former and current post-secondary literature students, suggest that though the selection of texts has widened to include postcolonial writers from India, the West Indies and Malaysia, students must be guided to identify and challenge their assumptions regarding literature in order to engage meaningfully with texts from different contexts. A key theme of this book is the foregrounding of literacy as experienced in Asia which, in the case of this chapter, plays out in the context of teaching and learning “Literature in English” in Malaysia. As a school subject in the Malaysian education system, “Literature in English” is offered as an elective for upper secondary (age 16–17) and post-secondary (age 18–19) school students. This chapter focuses on the latter, which has played a large role in shaping my literacy experience, first as a student of literature, then a teacher of literature, and now a researcher in literature education. In this chapter, I utilise two driving questions to highlight literacy practices in literature education. Based upon conceptualisations of the subject as expressed in the “Literature in English” syllabus, I ask, firstly, what texts students are expected to read, and secondly, how those texts should be read.
Science Education and Artificial Intelligence – A Chatbot on Magic and Quantum Computing as an Educational Tool

Artificial Intelligence (AI) has entered the realm of higher education and has become an impressive emerging field. Since AI is starting also to be applied to Science Education, here an assay is provided while the opportunities of AI in a particular case of Science Education are reviewed and assessed, namely a chatbot on Quantum Computing, another awesome emerging field. Indeed, Quantum Computing is based on a set of principles that are difficult to understand by the general population, so the ultimate goal of such a chatbot is to learn a few complex concepts in this field and to increase public understanding and awareness of Science. Actually, AI is starting also to be applied to Science Communication, even though progress is not so advanced as in learning.Magic tricks based on mathematical principles are especially well suited to teach difficult concepts, like those related to Quantum Science. They are used to explain entanglement, quantum cryptography, the superposition principle, and other quantum-related concepts – along with other physical and chemical core elements like Entropy.Developing a chatbot is not especially difficult (e.g., with Botpress, Watson, Dialogflow, Manybot, etc.). One may build either scripted, intelligent, or application bots. Indeed, the first ones are those that are easiest to create, and correspond to the first phase of the current project. Intelligent bots understand natural spoken language and correspond to a second phase in this project. In general, Bots are connected to a real-world messaging service, like Facebook Messenger, Twitter, Telegram, Slack, etc.
A Study on the Priority of 'Personal Information Safety Measure' Using AHP Method: Focus on the Defferences between Financial Company and Consignee

To survive in the trend of the fourth industrial revolution, companies are putting a lot of attention and effort into personalization services using the latest technologies such as big data, artificial intelligence and the Internet of Things, while entrusting third parties to handle personal information on the grounds of work efficiency, expertise and cost reduction. In such an environment, consignors need to check trustees on a more effective and reasonable basis to ensure personal information safety for trustees. This study used AHP techniques to derive the importance and priority of each item of Personal Information Safety Assurance Measures for financial companies and trustees, and objectively compared and analyzed differences in perceptions of importance between financial institutions and trustees. Based on this, the company recognizes the difference between self-inspection of financial institutions and inspection of trustees and presents policy grounds and implications for applying differentiated inspection standards that reflect the weights appropriate for the purpose.
Ransomware : Digitala kidnappare

This study about Ransomware uses a qualitative method to explore four Swedish IT- companies view on security and how to protect themselves against encroachment. The result shows that the companies ...
A Multi-Layer Arabic Text Steganographic Method Based on Letter Shaping

Text documents are widely used, however, the text steganography is more difficult than other media because of a little redundant information. This paper presents a text steganography methodology appropriate for Arabic Unicode texts that do not use a normal sequential inserting process to overcome the security issues of the current approaches that are sensitive to steg-analysis. The Arabic Unicode text is kept within main unshaped letters, and the proposed method is used text file as cover text to hide a bit in each letter by reshaping the letters according to its position (beginning, middle, end of the word, or standalone), this hiding process is accomplished through multi-embedding layer where each layer contains all words with the same Tag detected using the POS tagger, and the embedding layers are selected randomly using the stego key to improve the security issues. The experimental result shows that the purposed method satisfied the hiding capacity requirements, improve security, and imperceptibility is better than currently developed approaches.
Learning to Generate Questions by Recovering Answer-containing Sentences

To train a question answering model based on machine reading comprehension (MRC), significant effort is required to prepare annotated training data composed of questions and their answers from contexts. To mitigate this issue, recent research has focused on synthetically generating a question from a given context and an annotated (or generated) answer by training an additional generative model, which can be utilized to augment the training data. In light of this research direction, we propose a novel pre-training approach that learns to generate contextually rich questions, by recovering answer-containing sentences. Our approach is composed of two novel components, (1) dynamically determining K answers from a given document and (2) pre-training the question generator on the task of generating the answer-containing sentence. We evaluate our method against existing ones in terms of the quality of generated questions as well as the fine-tuned MRC model accuracy after training on the data synthetically generated by our method. Experimental results demonstrate that our approach consistently improves the question generation capability of existing models such as T5 and UniLM, and shows state-of-the-art results on MS MARCO and NewsQA, and comparable results to the state-of-the-art on SQuAD. Additionally, we demonstrate that the data synthetically generated by our approach is beneficial for boosting up the downstream MRC accuracy across a wide range of datasets, such as SQuAD-v1.1, v2.0, and KorQuAD, without any modification to the existing MRC models. Furthermore, our experiments highlight that our method shines especially when a limited amount of training data is given, in terms of both pre-training and downstream MRC data.
Audio Steganography Using Multi LSB and IGS Techniques

With advances in communication technologies and the flow of enormous amount of data, the need for providing a secure pathway for the same has become a necessity. In this paper, different methodologies are explored to find an efficient steganographic method for embedding one form of data into another, with the aim of enhancing data security. The proposed approach involves embedding an image into an audio signal. Image encryption methods are also explored with the intention of adding an extra layer of security to the system. The proposed system shows improvement in capacity and SNR. Various attacks are carried out on the stego signal to check the robustness of the resulting steganographic system.
Integrating complete gabor filter to the random forest classification algorithm for face recognition

Local feature approach to face recognition such as Local Binary Pattern (LBP) and Gabor has demonstrated to be an excellent facial descriptor. However, due to the large dimension features generated from the Gabor filter, the computation time for feature extraction is lengthy. Likewise, LBP generates lengthy histograms, which ultimately slow down the recognition time. This paper proposes a hybrid face recognition technique called Complete Gabor Filter with Random Forest (CG-RF) in biometrics technologies. CG-RF uses Gabor Magnitude Responses (GMR) and Oriented Gabor Phase Congruency Image (OGPCI) with Random Forest as features classification. For best features, Monte Carlo Uninformative Variable Elimination Partial Least Squares (MC-UVE-PLS) regression is used to select the important features generated from the Gabor filters. The upside of the proposed technique reduces computation time significantly without compromising on the accuracy of face recognition. Further experiments were conducted on Georgia Tech (GT) and Facial Evaluation Recognition Test (FERET) face databases with regards to varied face images such as head positions and orientations, occlusion and light illumination. The outcome of the experiments on both databases demonstrated a short turnaround in computation time and high recognition rates-GT (1735.2 seconds; 94.7%), FERET (8875.6 seconds; 96.7%).
Methodological approaches to the construction of a pedagogical system of educators' professional development

The article discusses the pedagogical system of educator's professional development in the process of research activity in a departmental educational organization as a complex of interconnected components, characterized by ways of integrating education and science. The essence of methodological approaches (personality-activity, personality-oriented, integrative and competency-based) to the construction of a system of educators' professional development in the process of scientific-research activity is revealed. Attention is paid to the principles and functions in the pedagogical system construction.
Digital Branch: Elementary Explorations: Pet or Stuffy Show and Tell

Explore the power of creativity. Take chances, make mistakes and have some fun. Come show off your pet or stuffy and see what other folks have as well. We will play a few games with our furry friends and have a fun hang out time.
PRESIMS: Requirements and design for preschool management system using usability theory / Nor Hasnul Azirah Abdul Hamid, Normalina Ibrahim@Mat and Nurul Najihah Mustopa

Student Information Management System (SIMS) is a computerized system for education that can be used to manage student information and data. PASTI An-Nur is chosen as a case study in developing the system. Thus, several problems are identified that PASTI An-Nur faces due to the implementation of a manual system in the admission process. The first problem is the paper-based registration form that is prone to lose, misplaced and less secure. As for the payment process, arise a problem in term of higher error rate when checking and calculating the payments. The biggest downfall for PASTI An-Nur is the amount of space used to store all the students' files. These problems bring inefficiency since the world is changing to computerized, where data management become one of the most significant issues nowadays. So, the aim of developing the Preschool Management System (PRESIMS) is for helping the staffs and teachers in managing the students' information. The Adapter Waterfall model was used in developing this system. Additionally, usability heuristics was used also as a theory to guide the development of this system. The system has been tested with the four (4) users and two (2) experts. The testing method is the ISO/IEC 9126- 4 approach to measure usability metrics, including efficiency, effectiveness, and satisfaction. Whereas, for the experts, heuristic evaluation is used to bring six (6) usability principles into implementation for testing. The result of the testing is very satisfying, which shows 75.5% of efficiency, 83.33% of effectiveness and three (3) out of four (4) users very satisfied with the system. The result of heuristic evaluation also shows a successful implementation of the system. The details of the result are discussed in this paper and expected to meet the users' specification and it is ready to go live.
Developing Intercultural Competence As A Strategic Goal Of English Language Teaching At Primary Schools

This article presents the problem of developing the ability of primary schoolchildren for the dialogue of cultures and to substantiate the concept of intercultural ability, or the ability to intercultural communication. Within the framework of this concept, primary foreign language education is interpreted as “a method and purposeful process of development and self-development of the personality of a younger student and his abilities for intercultural communication by managing a teacher and his educational activities, aimed at cognition and self-education, at revealing the potential of a student in the process of mastering a foreign language speech activity as a new way of communication in terms of interaction with a teacher, peers, including native speakers ”[3, p. 97].
Towards Robust Textual Representations with Disentangled Contrastive Learning

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks.
System and Network Administration

Learn the foundation of network and systemadministrationKnow the scope, duties and responsibilities of networkand system administratorKnow the network operation system that supportsystem administrationDistinguish the support of each network operating system
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios.

This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.
LibGuides: University of Limpopo Library Technical Services: U.L Institutional Repository (I.R) Online Tutorial

The Library Technical Services lib-guide aim to promote library services and facilitate access to materials for users within University of Limpopo (UL), its satellite campuses, and the world. UL IR. Online Tutorial
LibGuides: Primary Sources: Evaluating Primary Sources

How to find primary sources on the web and in the Broome Library. Evaluating Primary Sources
Investigation of a Novel Pick-and-Place Method

Acoustic levitation is a versatile and promising tool for object manipulation that has been improved signicantly in recent years. This master's thesis explores the potential use of acoustic levitat ...
Hide Me: Enabling Location Privacy in Heterogeneous Vehicular Networks

In order to support location-based services, vehicles share their location with a server to receive relevant data. Revealing a vehicle’s location compromises its privacy. One way to reduce this problem is obfuscating the vehicle’s location by adding artificial noise. However, this increases the area where the true location of the vehicle may be. Hence, under limited available bandwidth, the server will provide fewer data relevant to the vehicle’s true location, reducing the effectiveness of the location-based service. To compensate for this reduction, we allow that the data relevant to a vehicle is also shared through direct, ad hoc communication between neighboring vehicles. Through such Vehicle-to-Vehicle (V2V) cooperation, the impact of location obfuscation is mitigated. In this set up, and assuming that the data served may have different impact levels, we propose and study a game that determines the data subscription a vehicle should use, without explicit coordination among them. The aim is maximizing the expected impact of the data received, either directly from the server or via V2V. Our analysis and results show that the proposed V2V cooperation and derived strategy lead to significant performance increase compared to other uncoordinated approaches, and largely alleviates the impact of location obfuscation.