Mathematizing C++ concurrency

Shared-memory concurrency in C and C++ is pervasive in systems programming, but has long been poorly defined. This motivated an ongoing shared effort by the standards committees to specify concurrent behaviour in the next versions of both languages. They aim to provide strong guarantees for race-free programs, together with new (but subtle) relaxed-memory atomic primitives for high-performance concurrent code. However, the current draft standards, while the result of careful deliberation, are not yet clear and rigorous definitions, and harbour substantial problems in their details. In this paper we establish a mathematical (yet readable) semantics for C++ concurrency. We aim to capture the intent of the current (`Final Committee') Draft as closely as possible, but discuss changes that fix many of its problems. We prove that a proposed x86 implementation of the concurrency primitives is correct with respect to the x86-TSO model, and describe our Cppmem tool for exploring the semantics of examples, using code generated from our Isabelle/HOL definitions. Having already motivated changes to the draft standard, this work will aid discussion of any further changes, provide a correctness condition for compilers, and give a much-needed basis for analysis and verification of concurrent C and C++ programs

Tukey Regressive Hoover Indexed Deep Shift-Invariant Neural Network for Student Behavior Prediction

Prediction of student performance in the academic field creates significant challenges in developing reliable and accurate diagnosis models. Through the use of online learning behavior data, this paper may assist teachers in identifying students with learning challenges in advance and providing timely assistance. A novel technique called Tukey Regressive Hoover indexed Deep Shift Invariant Structure Neural Network (TRHIDSISNN) Model is introduced for student behaviour analysis with lesser time consumption. Initially, the student data and features are collected and transmitted to the input layer. After that, the features of collected student data are analyzed in hidden layer 1 with help of the Tukey Regression. The correlation between one or more independent features is identified to find the dependent feature. The relevant features are sent to the hidden layer 2. In that layer, the Hoover index is applied for analyzing the training and testing features. Finally, the hidden layer result is sent to the output layer where the hyperbolic tangent activation function is used to classify the data that belongs to that particular class. Based on the classification, the student grade level is predicted as high, medium and low based on their behavior gets displayed. Experimental assessment is carried out using different parameters such as prediction accuracy, false-positive rate, prediction time, and space complexity with respect to the number of student data.  The discussed results show that when compared to state-of-the-art approaches, the suggested TRHIDSISNN model achieves higher accuracy with shorter prediction times

Translating SIBI (Sign System for Indonesian Gesture) Gesture-to-Text in Real-Time using a Mobile Device

The SIBI gesture translation framework by Rakun was built using a series of machine learning technologies: MobileNetV2 for feature extraction, Conditional Random Field for finding the epenthesis movement frame, and Long Short-Term Memory for word classification. This high computational translation system was previously implemented on a personal computer system, which lacks portability and accessibility. This study implemented the system on a smartphone using an on-device inference method: the translation process is embedded into the smartphone to provide lower latency and zero data usage. The system was then improved using a parallel multi-inference method, which reduced the average translation time by 25%. The final mobile SIBI gesture-to-text translation system achieved a word accuracy of 90.560%, a sentence accuracy of 64%, and an average translation time of 20 seconds