Challenges of refugee teachers in Malaysian community-based learning centers

Because Malaysia is not a signatory to the 1951 RefugeeConvention, the children of refugees living in Malaysia are deprived of any formal education. Children are taught mostly by the refugees themselves, many of whom are volunteers. Most of the community-based learning centers, which encounter many academic and management problems, are also sustained by the refugees. This qualitative study aims to apply the framework of resources and demands theory to explore the work demands encountered by these refugees’ teachers and whether they have enough resources to meet the demands, as the learning centers are self-supported or supported by non-governmental organizations. This study collected data using the focus group method, recruiting participants through purposive sampling. Participants were organized into eight groups, each with seven volunteer participants, and open-ended questions were used for the participants to fully express their views and experiences. The data were analyzed using thematic analysis. The result showed that participants are burdened by academic and administrative tasks, lack of resources, poor infrastructure, and self-incompetence. The findings of the study proposed that more non-government organizations, local communities, and other stakeholders provide expertise and financial assistance to these community-based learning centers as education is the human right of each child. Karena Malaysia bukan merupakan penandatangan Konvensi Pengungsi 1951, hak anak-anak pengungsi untuk mendapat pendidikan resmi telah menjadi sebuah masalah yang tidak begitu diperhatikan. Kebanyakan anak-anak ini diajar oleh guru yang terdiri dari para pengungsi yang bekerja sebagai sukarelawan. Pusat pembelajaran berbasis komunitas dikelola dan dikendalikan oleh pengungsi sering menghadapi pelbagai isu dari segi akademik dan manajemen. Studi pendekatan kualitatif ini menggunakan teori sumber daya dan permintaan sebagai kerangka kajian untuk mengetahui apa saja tuntutan pekerjaan yang dihadapi guru pengungsi dan apakah mereka memiliki sumber daya yang cukup untuk menghadapi tuntutan tersebut, karena pusat pembelajaran bersifat swadaya atau didukung oleh Lembaga Swadaya Masyarakat (LSM). Sumber pembelajaran yang tersedia perlu dikaji supaya cukup menampung serta memenuhi permintaan kerja. Pengumpulan data menggunakan metode focus group dan partisipan direkrut melalui purposive sampling. Partisipan dikumpulkan ke delapan kelompok, masing-masing kelompok terdiri dari tujuh partisipan sukarelawan. Pertanyaan terbuka (open-ended questions) digunakan untuk mendapat pandangan serta pengalaman berkaitan isu yang dikaji. Data dianalisis menggunakan teknik analisis tematik. Hasil temuan menunjukkan bahwa para guru memikul beban yang berat dalam menjalankan tugas akademik serta administrasi. Mereka juga menghadapi isu kurangnya sumber daya, keadaan infrastruktur yang serba kekurangan serta keyakinan diri dalam kompetensi mengajar. Temuan studi ini mengusulkan agar lebih banyak LSM, masyarakat setempat, dan pemangku kepentingan lain menyumbangkan keahlian dan bantuan keuangan pada pusat-pusat pembelajaran tersebut karena pendidikan adalah hak asasi manusia bagi setiap anak-anak

Convolution channel separation and frequency sub-bands aggregation for music genre classification

In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framework that can extract and aggregate both short- and long-term features hierarchically. Our framework is based on ECAPA-TDNN, where all the layers that extract short-term features are affected by the layers that extract long-term features because of the back-propagation training. To prevent the distortion of short-term features, we devised the convolution channel separation technique that separates short-term features from long-term feature extraction paths. To extract more diverse features from our framework, we incorporated the frequency sub-bands aggregation method, which divides the input spectrogram along frequency bandwidths and processes each segment. We evaluated our framework using the Melon Playlist dataset which is a large-scale dataset containing 600 times more data than GTZAN which is a widely used dataset in MGC studies. As the result, our framework achieved 70.4% accuracy, which was improved by 16.9% compared to a conventional framework

Integrated Parameter-Efficient Tuning for General-Purpose Audio Models

The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for downstream tasks. Nevertheless, re-training all the parameters of these massive models entails an enormous amount of time and cost, along with a huge carbon footprint. To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain. We also propose an integrated parameter-efficient tuning (IPET) framework by aggregating the embedding prompt (a prompt-based learning approach), and the adapter (an effective transfer learning method). We demonstrate the efficacy of the proposed framework using two backbone pre-trained audio models with different characteristics: the audio spectrogram transformer and wav2vec 2.0. The proposed IPET framework exhibits remarkable performance compared to fine-tuning method with fewer trainable parameters in four downstream tasks: sound event classification, music genre classification, keyword spotting, and speaker verification. Furthermore, the authors identify and analyze the shortcomings of the IPET framework, providing lessons and research directions for parameter efficient tuning in the audio domain.Comment: 5 pages, 3 figures, submit to ICASSP202

One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification

The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Several studies have simply compressed SSL models through knowledge distillation (KD) without considering the target task. Consequently, these methods could not extract SV-tailored features. This paper suggests One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates KD and fine-tuning (FT). We optimize a student model for SV during KD training to avert the distillation of inappropriate information for the SV. OS-KDFT could downsize Wav2Vec 2.0 based ECAPA-TDNN size by approximately 76.2%, and reduce the SSL model's inference time by 79% while presenting an EER of 0.98%. The proposed OS-KDFT is validated across VoxCeleb1 and VoxCeleb2 datasets and W2V2 and HuBERT SSL models. Experiments are available on our GitHub

Tribological characteristics of high strength low alloy steel under various environmental conditions

High strength low alloy steel has excellent heat resistance and high strength. As it is commonly used as gun barrel material, a long service life and superior wear resistance are necessary for steel components. Here we investigated the wear characteristics of high strength low alloy steel surfaces under various environmental conditions, using a pin-on-disk wear test. Oxidation and wear debris effects on the coefficient of friction (COF) of the alloy steel were examined under air and argon (Ar) gas flow at atmospheric conditions

PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification

Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV systems to be less affected by noisy environments. The experimental results demonstrate that PAS outperforms traditional additive noise in terms of equal error rates (EER), with relative improvements of 4.64% and 5.01% observed in SE-ResNet34 and ECAPA-TDNN. We also show the effectiveness of proposed method by analyzing attention modules and visualizing speaker embeddings.Comment: 5 pages, 2 figures, 1 table, accepted to CKAIA2023 as a conference pape

An Open-Label, Multicenter, Phase I, Dose Escalation Study with Phase II Expansion Cohort to Determine the Safety, Pharmacokinetics, and Preliminary Antitumor Activity of Intravenous TKM-080301 in Subjects with Advanced Hepatocellular Carcinoma.

Lessons learnedTKM-080301 showed a favorable toxicity profile at the studied dose.TKM-080301 targeting PLK1 through small interfering RNA mechanism did not demonstrate improved overall survival in patients with advanced hepatocellular carcinoma compared with historical control. Preliminary antitumor activity as shown in this early-phase study does not support further evaluation as a single agent.BackgroundPolo-like kinase 1 (PLK1) is overexpressed in hepatocellular carcinoma (HCC). Knockdown of PLK1 expression by PLK1 small interfering RNA (siRNA) in an HCC cell line showed reduced expression in RNA-induced silencing complex and a reduction in cell proliferation.MethodsA 3 + 3 dose escalation plus expansion cohort at the maximum tolerated dose (MTD) was implemented. Patients with HCC, Eastern Cooperative Oncology Group (ECOG) performance status ≤2, and Child-Pugh score A received TKM-080301 as an intravenous infusion once every week for 3 consecutive weeks, repeated every 28 days.ResultsThe study enrolled 43 patients. The starting dose of TKM-080301 was 0.3 mg/kg, and MTD was declared at 0.75 mg/kg. Following the development of grade 4 thrombocytopenia in two subjects on the expansion cohort, the MTD was redefined at 0.6 mg/kg. Four patients did not have any evaluable postbaseline scan. Of the other 39 subjects who had received at least 0.3 mg/kg, 18 subjects (46.2%) had stable disease (SD) by independent RECIST 1.1 criteria. By Choi criteria, eight subjects (23.1%) had a partial response (PR). For 37 assessable subjects, with 2 subjects censored, median progression-free survival (PFS) was 2.04 months. Median survival for the whole study population was 7.5 months.ConclusionTKM-080301 was generally well tolerated. In this early-phase study, antitumor effect for TKM 080301 was limited. Further evaluation as a single agent in large randomized trials is not warranted

Molecular Subgroup Analysis of Clinical Outcomes in a Phase 3 Study of Gemcitabine and Oxaliplatin with or without Erlotinib in Advanced Biliary Tract Cancer

AbstractBACKGROUND: We previously reported that the addition of erlotinib to gemcitabine and oxaliplatin (GEMOX) resulted in greater antitumor activity and might be a treatment option for patients with biliary tract cancers (BTCs). Molecular subgroup analysis of treatment outcomes in patients who had specimens available for analysis was undertaken. METHODS: Epidermal growth factor receptor (EGFR), KRAS, and PIK3CA mutations were evaluated using peptide nucleic acid–locked nucleic acid polymerase chain reaction clamp reactions. Survival and response rates (RRs) were analyzed according to the mutational status. Sixty-four patients (48.1%) were available for mutational analysis in the chemotherapy alone group and 61 (45.1%) in the chemotherapy plus erlotinib group. RESULTS: 1.6% (2/116) harbored an EGFR mutation (2 patients; exon 20), 9.6% (12/121) harbored a KRAS mutation (12 patients; exon 2), and 9.6% (12/118) harbored a PIK3CA mutation (10 patients, exon 9 and 2 patients, exon 20). The addition of erlotinib to GEMOX in patients with KRAS wild-type disease (n = 109) resulted in significant improvements in overall response compared with GEMOX alone (30.2% vs 12.5%, P = .024). In 95 patients with both wild-type KRAS and PIK3CA, there was evidence of a benefit associated with the addition of erlotinib to GEMOX with respect to RR as compared with GEMOX alone (P = .04). CONCLUSION: This study demonstrates that KRAS mutational status might be considered a predictive biomarker for the response to erlotinib in BTCs. Additionally, the mutation status of PIK3CA may be a determinant for adding erlotinib to chemotherapy in KRAS wild-type BTCs