Reviewerly/exHarmony huggingface dataset:
Solo: a lightweight static analysis for differential privacy

Existing approaches for statically enforcing differential privacy in higher order languages use either linear or relational refinement types. A barrier to adoption for these approaches is the lack of support for expressing these “fancy types” in mainstream programming languages. For example, no mainstream language supports relational refinement types, and although Rust and modern versions of Haskell both employ some linear typing techniques, they are inadequate for embedding enforcement of differential privacy, which requires “full” linear types. We propose a new type system that enforces differential privacy, avoids the use of linear and relational refinement types, and can be easily embedded in richly typed programming languages like Haskell. We demonstrate such an embedding in Haskell, demonstrate its expressiveness on case studies, and prove soundness of our type-based enforcement of differential privacy.
Dealing with Class Imbalance in Sentiment Analysis Using Deep Learning and SMOTE

In textual data, sentiments or opinions expressing polarities (positive or negative) often form the basis for human decision-making. Therefore, sentiment analysis has always been an important area of research in the field of artificial intelligence. Recently, deep learning models have been used for the sentiment analysis. However, the class imbalance of the dataset adversely affects the performance of these models. To address this issue, the paper presents a method to resample the dataset using synthetic minority over-sampling technique (SMOTE). The proposed method is applied to three different datasets of customer reviews, each of which exhibits different class imbalance ratios. To show the impact of the method, the modern recurrent neural network (RNN)-based architectures (LSTM, GRU, and Bi-directional) are trained with both the originally imbalanced datasets and the SMOTE balanced datasets and comprehensively analyzed the performance of these approaches. The obtained results show that the models trained with balanced datasets outperform other methods across most models with significant improvements over the original dataset.
Femtosecond laser direct writing of photonic quantum logic gate chips for generating path-encoded entangled states

Femtosecond laser direct writing (FLDW) technique has been widely applied for fabrications of various photonic quantum chips in glass, such as chips for quantum computation, quantum walk, quantum simulation and quantum metrology. Photonic quantum logic gates are the building blocks for the realization of linear optical universal quantum computation. Up to now, several photonic quantum logic gates have been fabricated by FLDW, such as polarization encoded Hadamard (H) gate, Controlled NOT (CNOT) gate, herald CNOT gate and path encoded herald Controlled phase (CZ) gate and CNOT gate. By combining several single-qubit and two-qubit gates together, the constructed quantum circuits can realize some special functions, such as generating entangled states and perform quantum computation algorithms. Based on the successful fabrication of path encoded CNOT gate by FLDW, we further realized the fabrication of photonic quantum chips by cascading one H gate and one CNOT gate at the control qubit to generate path encoded Bell states, whose fidelity of truth table can reach 97.60.3%. Further, we cascaded one H gate and two parallel CNOT gates at the same control qubit to generate path encoded GHZ states, but which need three photons. Both Bell states and GHZ states are important entangled photon resources, which are widely used in quantum communication and quantum computation, and both combinations of logic gates above can be applied in many quantum circuits, so this work is of great importance and lays the technical foundation for the FLDW of more complex and powerful photonic quantum computation chips.
Strategies for Young College Teachers' Self-Realization

The research into social peculiarities, structure, and mechanisms of young teachers' self-realization is a relevant issue in modern sociological studies. The present paper features professional strategies that self-realization patterns. The research objective was to study strategies for professional self-realization employed by young teaching personnel of colleges. It was based on an authentic hybrid research complex, which included a combination of quantitative and qualitative analysis techniques: methods of included observation, in-depth interviews, expert interviews, and content analysis of regulatory documents. The analysis revealed three types of adaptive strategies for professional self-realization of young teachers: Strategy of Success, Strategy of Remonstrative Self-Realization, and Strategy of Forced Self-Realization. The article emphasizes the connection between social prosperity and the chosen strategy. Adaptive strategies force young teachers to comply with expectations of their social environment, which disagrees with the concept of self-realization. The humanistic non-adaptive strategy based on a free choice of patterns and modes of the teacher's work, which makes it possible to preserve physical and psychological health and save human resources.
OBU Library: Online Library Services: Home

This guide provides information regarding the library's remote services such as links to online resources and research assistance tools.
Effects of Illegal Artisanal Gold Mining Operations on Groundwater Quality in Ghana: The Case of Ahafo-Ano South District

The general properties and overall chemical quality for potability of groundwater in Ahafo-Ano South District of the Ashanti Region of Ghana have been evaluated. With respect to pH, about 92% of groundwaters were potable while 8% were acidic and not potable. Approximately 4%, 32%, 56% and 8% of sampled groundwater were soft, moderately soft, hard and very hard respectively. The overall chemical quality analysis of groundwaters showed that 20%, 28%, 40%, 4% and 8% had excellent, good, poor, very poor and unsuitable drinking water qualities respectively. Approximately 12%, 40% and 84% of As, Ni and Pb exceeded their respective WHO limits while 32% of Cd and Fe exceeded their respective limits for potable water. These higher concentrations of heavy metals were observed to have occurred in communities with intensive illegal gold mining operations. Inhabitants in these areas could potentially be more predisposed to potential health hazards including cancer, nervous system damage, low IQ in children, reduced growth of foetus and premature birth in pregnant women, and kidney damage. It is expected that illegal artisanal gold mining activities will be banned while policies aimed at providing alternative livelihood be instituted to minimize any potential health hazards on humans in the District.
Certifiable Artificial Intelligence Through Data Fusion

This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial intelligence (AI) systems. While the AI community has made rapid progress, there are challenges in certifying AI systems. Using procedures from design and operational test and evaluation, there are opportunities towards determining performance bounds to manage expectations of intended use. A notional use case is presented with image data fusion to support AI object recognition certifiability considering precision versus distance.
Deriving Frequent Itemsets from Lossless Condensed Representation

In data mining, major research topic is frequent itemset mining (FIM). Frequent Itemsets (FIs) usually generating a large amount of Itemsets from database it causing from high memory and long execution time usage. Frequent Closed Itemsets(FCI) and Frequent Maximal Itemsets(FMI) are a reduced lossless representation of frequent itemsets. The FCI allows to decreasing the memory usage and execution time while comparing to FMIs. The whole data of frequent Itemsets(FIs) may be derived from FCIs and FMIs with correct methods. While various study has presented several efficient approach for FCIs and FMIs mining. In sight of this, that we proposed an algorithm called DCFI-Mine for capably derive FIs from Closed FIs and RFMI algorithm derive FMIs to FIs. The advantages of DCFI-Mine algorithm has two features: First, efficiency, different existing algorithm that tends to develop an enormous quantity of Itemsets all through process, DCFI-Mine process the Itemsets straight without candidate generation. But in proposed RFMI multiple scan occurs due to search of item support so efficiency is less than proposed algorithm DCFI-Mine. Second, in terms of losslessness DCFI-Mine and RFMI can discover complete frequent itemset without lapse. Experimental result shows That DCFI-Mine is best deriving FIs in term of memory usage and executions time.
Improved Self Key Establishment Protocol for Multi Hop Communication under Wireless Device Systems

The wireless device systems are definitely the tiny systems composed of wireless device goods. The device tools catch environmentally friendly information. Information catch, the information receives and data gears are the three main procedures of the device products. The information morals will be sent through the radio rate of recurrence. Protection is the most important obligation for the purpose of the cellular device systems. Necessary administration is extremely important for anyone protection applications. The distribution procedure in administration has been changed drastically over the years. The self-key establishment process for cellular device sites (SKEW) is utilized to handle the crucial administration activities in terms of security. The effective self-key restaurant standard protocol provides essential administration with much less important storage space, conversation, transmitting regularity and computational expenses. The crucial circulation can be performed with minimal message transmitting procedure. The self-key store process with regards to cellular device systems can be the foundation process for everybody protection protocols. The self-key establishment protocol is mainly created for the single-hop communication environment. The proposed platform enhances the self-key organization process for numerous jump conversation environments. The SKEW process can be sustained for unreserved multiple bottom train station environment without groupings. The self-key business process shall be built-in with additional protection protocols to offer a comprehensive option for essential distribution and guaranteed conversation procedure
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the first effort to assign mixed quantization schemes and multiple precisions within layers -- among rows of the DNN weight matrix, for simplified operations in hardware inference, while preserving accuracy. Furthermore, this paper makes a different observation from the prior work that the quantization error does not necessarily exhibit the layer-wise sensitivity, and actually can be mitigated as long as a certain portion of the weights in every layer are in higher precisions. This observation enables layer-wise uniformality in the hardware implementation towards guaranteed inference acceleration, while still enjoying row-wise flexibility of mixed schemes and multiple precisions to boost accuracy. The candidates of schemes and precisions are derived practically and effectively with a highly hardware-informative strategy to reduce the problem search space. With the offline determined ratio of different quantization schemes and precisions for all the layers, the RMSMP quantization algorithm uses the Hessian and variance-based method to effectively assign schemes and precisions for each row. The proposed RMSMP is tested for the image classification and natural language processing (BERT) applications and achieves the best accuracy performance among state-of-the-arts under the same equivalent precisions. The RMSMP is implemented on FPGA devices, achieving 3.65x speedup in the end-to-end inference time for ResNet-18 on ImageNet, compared with the 4-bit Fixed-point baseline.
Software Quality Assesment using COCOMO II Metrics with ABC and NN

Time, cost and quality predictions are the key aspects of any software development system. Loses that result due to wrong estimations may lead to irresistible damage. It is observed that a badly estimated project always results into a bad quality output as the efforts are put in the wrong direction. In the present study, author proposed ABC-COCOMO-II as a new model and tried to enhance the extent of accuracy in effort quality assessment through effort estimation. In the proposed model author combined the strengths of COCOMO-II (Constructive Cost Model) with the Artificial Bee Colony (ABC) and Neural Network (NN). In the present work, ABC algorithm is used to select the best solution, NN is used for the classification purpose to improve the quality estimation using COCOMO-II. The results are compared and evaluated with the pre-existing effort estimation models. The simulation results had shown that the proposed combination outperformed in terms of quality estimation with small variation of 5-10% in comparison to the actual effort, which further leads to betterment of the quality. More than 90% projects results into high quality output for the proposed algorithmic architecture.
MoveRL: To a Safer Robotic Reinforcement Learning Environment

The deployment of Reinforcement Learning (RL) on physical robots still stumbles on several challenges, such as sample-efficiency, safety, reproducibility, cost, and software platforms. In this paper, we introduce MoveRL, an environment that exposes a standard OpenAI Gym interface, and allows any off-the-shelf RL agent to control a robot built on ROS, the Robot OS. ROS is the standard abstraction layer used by roboticists, and allows to observe and control both simulated and physical robots. By providing a bridge between the Gym and ROS, our environment allows an easy evaluation of RL algorithms in highly-accurate simulators, or real-world robots, without any change of software. In addition to a Gym-ROS bridge, our environment also leverages MoveIt, a state-of-the-art collision-aware robot motion planner, to prevent the RL agent from executing actions that would lead to a collision. Our experimental results show that a standard PPO agent is able to control a simulated commercial robot arm in an environment with moving obstacles, while almost perfectly avoiding collisions even in the early stages of learning. We also show that the use of MoveIt slightly increases the sample-efficiency of the RL agent. Combined, these results show that RL on robots is possible in a safe way, and that it is possible to leverage state-of-the-art robotic techniques to improve how an RL agent learns. We hope that our environment will allow more (future) RL algorithms to be evaluated on commercial robotic tasks. Github repository: https://github.com/Gaoyuan-Liu/MoveRL
Pseudo-random number generation based on digit isolation referenced to entropy buffers

Unpredictable pseudo-random number generators (PRNGs) are presented based on dissociated components with only coincidental interaction. The first components involve pointers taken from series of floating point numbers (float streams) arising from arithmetic. The pointers are formed by isolating generalized digits sufficiently far from the most significant digits in the float streams and may be combined into multi-digit pointers. The pointers indicate draw locations from the second component which are entropy decks having one or more cards corresponding to the elements used to assemble random numbers. Like playing cards, decks are cut and riffle-shuffled based on rules using digits appearing in the simulations. The various ordering states of the cards provide entropy to the PRNGs. The dual nature of the PRNGs is novel since they can operate either entirely on pointer variability to fixed decks or on shuffling variability using fixed pointer locations. Each component, pointers and dynamic entropy, is dissociated from the other and independently shown to pass stringent statistical tests with the other held as fixed; a “gold standard” mode involves changing the coincidental interaction between these two strong emulators of randomness by either cutting or shuffling prior to each draw. Gold standard modes may be useful in cryptography and in assessing tests themselves. One PRNG contains [Formula: see text] states in the entropy pool, another generates integers approximately 50% faster than the Advanced Encryption Standard (AES) PRNG with similar empirical performance, and a third generates full double-precision floats at speeds comparable to unsigned integer rates of the AES PRNG.
Augmented reality smart glasses as assembly operator support : Towards a framework for enabling industrial integration

Operators are likely to continue to play an integral part in industrial assembly for the foreseeable future. This is in part because increasingly shorter life-cycles and increased variety of produc ...
A Novel Multi Hidden Layer Convolutional Neural Network for Content Based Image Retrieval

The applications of a content-based image retrieval system in fields such as multimedia, security, medicine, and entertainment, have been implemented on a huge real-time database by using a convolutional neural network architecture. In general, thus far, content-based image retrieval systems have been implemented with machine learning algorithms. A machine learning algorithm is applicable to a limited database because of the few feature extraction hidden layers between the input and the output layers. The proposed convolutional neural network architecture was successfully implemented using 128 convolutional layers, pooling layers, rectifier linear unit (ReLu), and fully connected layers. A convolutional neural network architecture yields better results of its ability to extract features from an image. The Euclidean distance metric is used for calculating the similarity between the query image and the database images. It is implemented using the COREL database. The proposed system is successfully evaluated using precision, recall, and F-score. The performance of the proposed method is evaluated using the precision and recall.
The Monitoring of Dirichlet Compositional Data

Compositional data are used in many applications such as Cement, Asphalt, and many other Chemical industries.Such data represent random variables whose values must sum up to a certain constant.Quality engineers and technicians require monitoring compositional data and detecting the source of the irregularity in the process as soon as it happens.Throughout the literature, complicated methods were introduced to monitor compositional data.Such methods are computationally complex and can lead to difficulties in interpreting the results.The Dirichlet distribution is commonly used in the literature to model compositional data.In this study, we propose three simple methods to monitor the mean vector of the Dirichlet distribution.The first method is based on a MEWMA control chart.The second method is based on transforming the Dirichlet random variables into beta random variables and then monitoring them using multiple EWMA control charts, while the third method uses multiple EWMA control charts for transformed independent random variables.Using a simulation technique, the performance of the three methods is investigated, and the three methods performed very well under different sample sizes, many random variables, and values of the distribution parameters.When the process is out-of-control, the source of the out-of-control signal can be detected using Method 2 and Method 3. Method 2 maintained its good performance with a probability 0.99 of correctly detecting the source of the signal.Method 3 performed well except for the case of Dirichlet parameter values less than one.However, it maintained almost a probability of correct detection of at least 90% in most cases.The three proposed methods are simple, do not need complicated calculations, and can easily be applied and used by practitioners.
A Two Factor Image Based Authentication System

In the world, mobile phone usage has increased rapidly in the world, resulting in more security issues and requiring security improvements. The data from the mobile phones are stolen or deleted from the mobile phones of the users. Even though their mobile phones are protected with device-in-built security, they can be easily cracked by shoulder surfing, random guessing, or brute force attack. The paper aims to protect the users from these problems by using Images with pattern-based security with Image CAPTCHA for Authentication. The user uses this application to set a graphical password by moving images in a matrix in a particular pattern, locking their data. The user has given different types of images, and the user has to select any three different images. Then the user has to draw the password for the selected image. This will enable the highest security to the mobile/web. By this risk of losing data will be less. It also has two-phase authentication security. When a user has to open the mobile/web, then the user has to enter any one of the passwords that the user has selected at the registration phase. After entering the password, the user has to click on the screen when he has chosen during the registration phase.
ListReader: Extracting List-form Answers for Opinion Questions

Question answering (QA) is a high-level ability of natural language processing. Most extractive ma-chine reading comprehension models focus on factoid questions (e.g., who, when, where) and restrict the output answer as a short and continuous span in the original passage. However, in real-world scenarios, many questions are non-factoid (e.g., how, why) and their answers are organized in the list format that contains multiple non-contiguous spans. Naturally, existing extractive models are by design unable to answer such questions. To address this issue, this paper proposes ListReader, a neural ex-tractive QA model for list-form answer. In addition to learning the alignment between the question and content, we introduce a heterogeneous graph neural network to explicitly capture the associations among candidate segments. Moreover, our model adopts a co-extraction setting that can extract either span- or sentence-level answers, allowing better applicability. Two large-scale datasets of different languages are constructed to support this study. Experimental results show that our model considerably outperforms various strong baselines. Further discussions provide an intuitive understanding of how our model works and where the performance gain comes from.
Discussion on domain generalization in the cross-device speaker verification system

In this paper, we use domain generalization to improve the performance of the cross-device speaker verification system. Based on a trainable speaker verification system, we use domain generalization algorithms to fine-tune the model parameters. First, we use the VoxCeleb2 dataset to train ECAPA-TDNN as a baseline model. Then, use the CHT-TDSV dataset and the following domain generalization algorithms to fine-tune it: DANN, CDNN, Deep CORAL. Our proposed system tests 10 different scenarios in the NSYSU-TDSV dataset, including a single device and multiple devices. Finally, in the scenario of multiple devices, the best equal error rate decreased from 18.39 in the baseline to 8.84. Successfully achieved cross-device identification on the speaker verification system.