Towards a pedagogy of reconciliation and transformation: A peace education through art initiative in response to 26/11

The 26/11 Mumbai attacks in India severely impacted the already strained Indo–Pak political relations and fuelled prejudice against the common people of Pakistan. Since the attacks, Indian people have found various expressions of collective memory and ways to commemorate the incident. While these serve as a remembrance of the attack, it also reinforces negative attitudes towards Pakistan and its people, hindering any prospects of peace and reconciliation. This article describes a peace education through art initiative implemented in a high school in Mumbai. It draws from a synergy of theoretical concepts in peace, reconciliation and conflict transformation for its curricular framework that has three inquiry processes: Examine–Envision–Envisage. This article describes the implementation and outcomes of the initiative that support the value of an integrated peace- and reconciliation-focused art education pedagogy aimed at promoting reconciliation in relation to ongoing/intractable conflicts. Furthermore, it highlights the importance of addressing negative emotions inherent in ongoing conflicts and how empathy might contribute towards reducing prejudice towards the ‘Other’.

Study of human knowledge and attitude toward urban birds in Faisalabad city, Pakistan

Birds are very valuable indicators of species richness and endemic patterns in a specified ecosystem, which eventually help the scientist to measure the environmental degradation. The aim of present study was to know human knowledge and attitude toward urban birds in Faisalabad city, Pakistan. The study conducted in four consecutive months: November 2019 to February 2020. Population of birds was noted from eight residential towns of Faisalabad city, data were collected through questionnaire. Faisalabad has a reasonably large population of birds and present data show that, there is a significant difference between favorite bird of residential areas and institutions. The pigeon received the most likeness in bird population among residential area residents, while the myna received the least. The most popular bird in Faisalabad institutions was the sparrow, while the least popular bird was the common myna. Bird adaptation percentage of residential areas and institutional areas of Faisalabad was the highest for parrot and sparrow respectively. People in residential areas and institutions, on the other hand, adapted least to common myna. It is concluded that people of the study area like birds and offered food and high population of birds are present in study area.

Shared Secret Key Generation by Exploiting Inaudible Acoustic Channels

To build a secure wireless networking system, it is essential that the cryptographic key is known only to the two (or more) communicating parties. Existing key extraction schemes put the devices into physical proximity and utilize the common inherent randomness between the devices to agree on a secret key, but they often rely on specialized hardware (e.g., the specific wireless NIC model) and have low bit rates. In this article, we seek a key extraction approach that only leverages off-the-shelf mobile devices, while achieving significantly higher key generation efficiency. The core idea of our approach is to exploit the fast varying inaudible acoustic channel as the common random source for key generation and wireless parallel communication for exchanging reconciliation information to improve the key generation rate. We have carefully studied and validated the feasibility of our approach through both theoretical analysis and a variety of measurements. We implement our approach on different mobile devices and conduct extensive experiments in different real scenarios. The experiment results show that our approach achieves high efficiency and satisfactory robustness. Compared with state-of-the-art methods, our approach improves the key generation rate by 38.46% and reduces the bit mismatch ratio by 42.34%.

TLB-pilot: Mitigating TLB Contention Attack on GPUs with Microarchitecture-Aware Scheduling

Co-running GPU kernels on a single GPU can provide high system throughput and improve hardware utilization, but this raises concerns on application security. We reveal that translation lookaside buffer (TLB) attack, one of the common attacks on CPU, can happen on GPU when multiple GPU kernels co-run. We investigate conditions or principles under which a TLB attack can take effect, including the awareness of GPU TLB microarchitecture, being lightweight, and bypassing existing software and hardware mechanisms. This TLB-based attack can be leveraged to conduct Denial-of-Service (or Degradation-of-Service) attacks. Furthermore, we propose a solution to mitigate TLB attacks. In particular, based on the microarchitecture properties of GPU, we introduce a software-based system, TLB-pilot, that binds thread blocks of different kernels to different groups of streaming multiprocessors by considering hardware isolation of last-level TLBs and the application’s resource requirement. TLB-pilot employs lightweight online profiling to collect kernel information before kernel launches. By coordinating software- and hardware-based scheduling and employing a kernel splitting scheme to reduce load imbalance, TLB-pilot effectively mitigates TLB attacks. The result shows that when under TLB attack, TLB-pilot mitigates the attack and provides on average 56.2% and 60.6% improvement in average normalized turnaround times and overall system throughput, respectively, compared to the traditional Multi-Process Service based co-running solution. When under TLB attack, TLB-pilot also provides up to 47.3% and 64.3% improvement (41% and 42.9% on average) in average normalized turnaround times and overall system throughput, respectively, compared to a state-of-the-art co-running solution for efficiently scheduling of thread blocks.

Prediction of Football Match Results Based on Edge Computing and Machine Learning Technology

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

Multi-task Fuzzy Clustering–Based Multi-task TSK Fuzzy System for Text Sentiment Classification

Text sentiment classification is an important technology for natural language processing. A fuzzy system is a strong tool for processing imprecise or ambiguous data, and it can be used for text sentiment analysis. This article proposes a new formulation of a multi-task Takagi-Sugeno-Kang fuzzy system (TSK FS) modeling, which can be used for text sentiment image classification. Using a novel multi-task fuzzy c-means clustering algorithm, the common (public) information among all tasks and the individual (private) information for each task are extracted. The information about clustering, for example, cluster centers, can be used to learn the antecedent parameters of multi-task TSK fuzzy systems. With the common and individual antecedent parameters obtained, a corresponding multi-task learning mechanism for learning consequent parameters is devised. Accordingly, a multi-task fuzzy clustering–based multi-task TSK fuzzy system (MTFCM-MT-TSK-FS) is proposed. When the proposed model is built, the information conveyed by the fuzzy rules formed is two-fold, including (1) common fuzzy rules representing the inter-task correlation information and (2) individual fuzzy rules depicting the independent information of each task. The experimental results on several text sentiment datasets demonstrate the validity of the proposed model.

LEXICAL AND ETHNOGRAPHIC DESCRIPTION OF THE KHAKASS TRADITIONAL DWELLINGS

В статье охарактеризованы лексемы, называющие традиционные жилища хакасов, а также дается их описание в этнографическом аспекте. Рассмотрены такие слова, как иб ‘дом; жилище, изба’, киис иб ‘войлочная юрта’, тос иб ‘берестяная юрта’, тирмелг иб ‘решетчатая юрта’, хараачылыг иб ‘юрта с обручем’, ахпайзац иб (фольк.) ‘богатый дом; дворец’, орге ‘дворец’, тура ‘здание; дом, изба’, иб - чурт ‘хозяйство; усадьба’, соол ‘избушка с особой печкой’, алачых ‘летнее хакасское жилище конусообразной формы’, этн. ‘временный свадебный шалаш’, одаг ‘шалаш’. Общетюркский пласт лексики составляют следующие наименования жилищ: иб, тура, чурт, алачых, одаг. Разнотипность построек находит отражение и в хакасских наименованиях жилых и хозяйственных построек. The article describes the lexemes that name traditional dwellings of the Khakass people, and also gives their description in an ethnographic aspect. Such words like ib ‘house; dwelling, hut’, kiis ib ‘felt yurt’, tos ib ‘birch yurt’, tirmelig ib ‘latticework yurt’, kharaachylyg ib ‘yurt with a hoop’, akh paizan ib (folk.) ‘rich house; palace’, yorge ‘palace’, tura ‘building; house, hut’, ib - churt ‘farm; farmstead’, sool ‘hut with a special stove’, alachykh ‘summer Khakass dwelling of a conical shape’, ethn. ‘temporary wedding hovel’, odag ‘hovel’ are considered. The common layer of vocabulary comprises the following names of dwellings: ib, tura, churt, alachykh, odag. The diversity of buildings is reflected in Khakass names of residential and commercial buildings.

Classification of Stages Diabetic Retinopathy Using MobileNetV2 Model

Diabetic retinopathy (DR) is a complication of diabetes mellitus that causes retinal damage that can lead to vision loss if not detected and treated promptly. The common diagnosis stages of the disease take time, effort, and cost and can be misdiagnosed. In the recent period with the explosion of artificial intelligence, deep learning has become the most popular tool with high performance in many fields, especially in the analysis and classification of medical images. The Convolutional Neural Network (CNN) is more widely used as a deep learning method in medical imaging analysis with highly effective. In this paper, the five-stage image of modern DR (healthy, mild, moderate, severe, and proliferative) can be detected and classified using the deep learning technique. After cross-validation training and testing on the corresponding 5,590-image dataset, a pre-MobileNetV2 training model is proposed in classifying stages of diabetic retinopathy. The average accuracy of the model achieved was 93.89% with the precision of 94.00%, recall 92.00% and f1-score 90.00%. The corresponding thermal image is also given to help experts for evaluating the influence of the retina in each different stage.