An Adaptive Application Framework with Customizable Quality Metrics

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.

The Use of Explicit Instruction in Writing Amongst Students of Colleges of Education in the Ashanti Region of Ghana

The study sought to examine the use of Explicit Instruction in writing lessons at some selected Colleges of Education in the Ashanti Region of Ghana. The collective case study design informed by constructivist grounded theory data analysis methods was used. Data were collected and analyzed using three instruments namely a semi-structured interview, sample texts on argumentative and expository essays and observations. The study revealed that combining the cognitive strategy of text structure knowledge application with the metacognitive strategy of self-monitoring supports the development of academic writing in students in the Colleges of Education. Also, students make mistakes in their writing and these mistakes include verb errors, article errors and wrong words. It was also revealed that with regards to Explicit Instruction in the classroom, tutors comprehensively used instructions in the language class to enhance students writing skills. This study further showed that tutors have a variety of evidence-based instructional practices that improve many different skills and student’s writing knowledge. The researcher recommended that students in Colleges of Education in Ghana are made to read extensively outside the curriculum to broaden their vocabulary repertoire so that the over-reliance on tutors for corrections can be minimized.

Masking Feedforward Neural Networks Against Power Analysis Attacks

Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.

Distinct replay signatures for planning and memory maintenance

Theories of neural replay propose that it supports a range of different functions, most prominently planning and memory maintenance. Here, we test the hypothesis that distinct replay signatures relate to planning and memory maintenance. Our reward learning task required human participants to utilize structure knowledge for 'model-based' evaluation, while maintaining knowledge for two independent and randomly alternating task environments. Using magnetoencephalography (MEG) and multivariate analysis, we found neural evidence for compressed forward replay during planning and backward replay following reward feedback. Prospective replay strength was enhanced for the current environment when the benefits of a model-based planning strategy were higher. Following reward receipt, backward replay for the alternative, distal environment was enhanced as a function of decreasing recency of experience for that environment. Consistent with a memory maintenance role, stronger maintenance-related replay was associated with a modulation of subsequent choices. These findings identify distinct replay signatures consistent with key theoretical proposals on planning and memory maintenance functions, with their relative strength modulated by on-going computational and task demands.

Digital control of the application efficiency of agricultural land and the involvement of retired territories into circulation

The economic instability of recent decades has various social consequences. One of which is the emergence of abandoned agricultural areas. Analytical studies have shown that geolocation, aimed at creating a digital image of agricultural areas, reveals exclusion zones. The government has set the task of developing programs to involve the agricultural turnover of unused land. It is proposed to carry out a system-architectural design of the target zoning of territories. The selection of the basic model within the architecture of the Database of agricultural lands in circulation is supplemented by a block of a digital image for recognizing resource opportunities. The problem of developing a formalized set of typed commands that structure knowledge about the state of territories and their functionality for expert programming systems is solved. The database is formed from declarative (factual), procedural, and control knowledge. To form the database, the state of unused agricultural land in the country was shown. A forecast was given by the Ministry of Agriculture of Russia for the involvement of fallow lands in the turnover by the end of 2030. The concept of the efficiency of involving unused agricultural land into circulation was presented. A mathematical description of the risks and a graphical presentation of ways to achieve the efficiency of returning unused land by a set of indicators of risk restrictions were given. The key advantage of the developed concept is the creation of a modular-type production infrastructure, which is modernized and filled according to the current needs of economic activity, which is flexible to changes in internal factors and does not require external resources.

Modeling hypotactic structure for Chinese-English neural machine translation of complex sentences

The hypotactic structural relation between clauses plays an important role in improving the discourse coherence of document-level translation. However, the standard neural machine translation (NMT) models do not explicitly model the hypotactic relationship between clauses, which usually leads to structurally incorrect translations of long and complex sentences. This problem is particularly noticeable on Chinese-to-English translation task of complex sentences due to the grammatical form distinction between English and Chinese. English is rich in grammatical form (e.g. verb morphological changes and subordinating conjunctions) while Chinese is poor in grammatical form. These linguistic phenomena make it a challenge for NMT to learn the hypotactic structure knowledge from Chinese as well as the structure alignment between Chinese and English. To address these issues, we propose to model the hypotactic structure for Chinese-to-English complex sentence translation by introducing hypotactic structure knowledge. Specifically, we annotate and build a hypotactic structure aligned parallel corpus that provides rich hypotactic structure knowledge for NMT. Moreover, we further propose a structure-infused neural framework to combine the hypotactic structure knowledge with the NMT model through two integrating strategies. In particular, we introduce a specific structure-aware loss to encourage the NMT model to better learn the structure knowledge. Experimental results on WMT17, WMT18 and WMT19 Chinese-to-English translation tasks demonstrate the effectiveness of the proposed methods.

What Happened to Roth’s Design Catalogues? - A Review of Usage and Future Research

In 1971, Roth and his group first proposed an algorithmic selection procedure for the design of mechanical systems using catalogues. Core element were Design Catalogues that provide established solution elements, models and operations for different engineering tasks. In different books and guidelines, the theory of Design Catalogues was promoted and comprehensive catalogues were elaborated. These works highlight the basic character as structured information bases, with knowledge and access criteria tailored to the needs of engineering tasks. An essential characteristic is the consequent classification of solutions, objects, and operations and thus a complete exploration of the area of interest. In science and industrial practice, Design Catalogues were recognized as tools to structure knowledge and improve reuse of solutions, operations or objects that are frequently used in the design process. This contribution analyses the use and evolution of Design Catalogues in the past 50 years. Main objective is to point out how Design Catalogues and underlying principles and tools to structure design knowledge were used in different fields of application. Moreover, future fields of research to classify knowledge elements and identify suitable access criteria to build up Design Catalogues will be pointed out.